diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index b75a383f..ee9a1502 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -48,4 +48,5 @@ repos:
     hooks:
       - id: mypy
         additional_dependencies: [types-PyYAML, types-Pillow, types-tqdm]
+        files: ^neural_lam/
         description: Check for type errors
diff --git a/CHANGELOG.md b/CHANGELOG.md
index ccf79fdb..8e15ab0e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -74,6 +74,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Maintenance
 
+- Enforce 100% type-hint coverage across `neural_lam/` via a `[tool.mypy]` gate (`disallow_untyped_defs`/`disallow_incomplete_defs`) and align all type annotations with PEP 585 and PEP 604 [\#673](https://github.com/mllam/neural-lam/pull/673) @GiGiKoneti
+
 - Add comprehensive type hints to GraphLAM in `neural_lam/models/step_predictors/graph/graph_lam.py` [\#669](https://github.com/mllam/neural-lam/pull/669) @GiGiKoneti
 
 - Add comprehensive type hints to ARForecaster in `neural_lam/models/forecasters/autoregressive.py` [\#663](https://github.com/mllam/neural-lam/pull/663) @GiGiKoneti
diff --git a/neural_lam/config.py b/neural_lam/config.py
index 1da43fff..a449085a 100644
--- a/neural_lam/config.py
+++ b/neural_lam/config.py
@@ -3,7 +3,7 @@
 # Standard library
 import dataclasses
 from pathlib import Path
-from typing import Dict, Union
+from typing import cast
 
 # Third-party
 import dataclass_wizard
@@ -35,7 +35,7 @@ class DatastoreSelection:
     kind: str
     config_path: str
 
-    def __post_init__(self):
+    def __post_init__(self) -> None:
         """
         Validate that the selected datastore kind is implemented.
 
@@ -56,11 +56,11 @@ class ManualStateFeatureWeighting:
 
     Attributes
     ----------
-    weights : Dict[str, float]
+    weights : dict[str, float]
         Manual weights for the state features.
     """
 
-    weights: Dict[str, float]
+    weights: dict[str, float]
 
 
 @dataclasses.dataclass
@@ -80,14 +80,14 @@ class OutputClamping:
 
     Attributes
     ----------
-    lower : Dict[str, float]
+    lower : dict[str, float]
         The minimum value to clamp each output feature to.
-    upper : Dict[str, float]
+    upper : dict[str, float]
         The maximum value to clamp each output feature to.
     """
 
-    lower: Dict[str, float] = dataclasses.field(default_factory=dict)
-    upper: Dict[str, float] = dataclasses.field(default_factory=dict)
+    lower: dict[str, float] = dataclasses.field(default_factory=dict)
+    upper: dict[str, float] = dataclasses.field(default_factory=dict)
 
 
 @dataclasses.dataclass
@@ -97,8 +97,8 @@ class TrainingConfig:
 
     Attributes
     ----------
-    state_feature_weighting : Union[ManualStateFeatureWeighting,
-                                    UniformFeatureWeighting]
+    state_feature_weighting :
+        ManualStateFeatureWeighting | UniformFeatureWeighting
         The method to use for weighting the state features in the loss
         function. Defaults to uniform weighting (`UniformFeatureWeighting`, i.e.
         all features are weighted equally).
@@ -107,9 +107,9 @@ class TrainingConfig:
         Defaults to an empty ``OutputClamping`` (no clamping).
     """
 
-    state_feature_weighting: Union[
-        ManualStateFeatureWeighting, UniformFeatureWeighting
-    ] = dataclasses.field(default_factory=UniformFeatureWeighting)
+    state_feature_weighting: (
+        ManualStateFeatureWeighting | UniformFeatureWeighting
+    ) = dataclasses.field(default_factory=UniformFeatureWeighting)
 
     output_clamping: OutputClamping = dataclasses.field(
         default_factory=OutputClamping
@@ -174,7 +174,7 @@ class InvalidConfigError(Exception):
 
 def load_config_and_datastore(
     config_path: str,
-) -> tuple[NeuralLAMConfig, Union[MDPDatastore, NpyFilesDatastoreMEPS]]:
+) -> tuple[NeuralLAMConfig, MDPDatastore | NpyFilesDatastoreMEPS]:
     """
     Load the neural-lam configuration and the datastore specified in the
     configuration.
@@ -186,7 +186,7 @@ def load_config_and_datastore(
 
     Returns
     -------
-    tuple[NeuralLAMConfig, Union[MDPDatastore, NpyFilesDatastoreMEPS]]
+    tuple[NeuralLAMConfig, MDPDatastore | NpyFilesDatastoreMEPS]
         The Neural-LAM configuration and the loaded datastore.
     """
     try:
@@ -204,4 +204,4 @@ def load_config_and_datastore(
         datastore_kind=config.datastore.kind, config_path=datastore_config_path
     )
 
-    return config, datastore
+    return config, cast(MDPDatastore | NpyFilesDatastoreMEPS, datastore)
diff --git a/neural_lam/create_graph.py b/neural_lam/create_graph.py
index c67e4397..faed9c70 100644
--- a/neural_lam/create_graph.py
+++ b/neural_lam/create_graph.py
@@ -3,7 +3,6 @@
 # Standard library
 import os
 from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
-from typing import Optional
 
 # Third-party
 import matplotlib
@@ -22,7 +21,7 @@
 
 
 def plot_graph(
-    graph: pyg.data.Data, title: Optional[str] = None
+    graph: pyg.data.Data, title: str | None = None
 ) -> tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]:
     """
     Render a PyTorch Geometric graph using stored node coordinates.
@@ -266,10 +265,10 @@ def prepend_node_index(graph: networkx.Graph, new_index: int) -> networkx.Graph:
 def create_graph(
     graph_dir_path: str,
     xy: np.ndarray,
-    n_max_levels: Optional[int] = None,
-    hierarchical: Optional[bool] = False,
-    create_plot: Optional[bool] = False,
-):
+    n_max_levels: int | None = None,
+    hierarchical: bool = False,
+    create_plot: bool = False,
+) -> None:
     """
     Create graph components from `xy` grid coordinates and store in
     `graph_dir_path`.
@@ -488,8 +487,10 @@ def create_graph(
                 .reshape((n, n, 2))[1::nx, 1::nx, :]
                 .reshape(int(n / nx) ** 2, 2)
             )
-            ij = [tuple(x) for x in ij]
-            G[lev] = networkx.relabel_nodes(G[lev], dict(zip(G[lev].nodes, ij)))
+            ij_tuples = [tuple(x) for x in ij]
+            G[lev] = networkx.relabel_nodes(
+                G[lev], dict(zip(G[lev].nodes, ij_tuples))
+            )
             G_tot = networkx.compose(G_tot, G[lev])
 
         # Relabel mesh nodes to start with 0
@@ -647,10 +648,10 @@ def create_graph(
 def create_graph_from_datastore(
     datastore: BaseRegularGridDatastore,
     output_root_path: str,
-    n_max_levels: Optional[int] = None,
+    n_max_levels: int | None = None,
     hierarchical: bool = False,
     create_plot: bool = False,
-):
+) -> None:
     """
     Generate graph components for ``datastore`` and persist them on disk.
 
@@ -683,7 +684,7 @@ def create_graph_from_datastore(
     )
 
 
-def cli(input_args: Optional[list[str]] = None) -> None:
+def cli(input_args: list[str] | None = None) -> None:
     """
     Parse CLI arguments and call :func:`create_graph_from_datastore`.
 
diff --git a/neural_lam/custom_loggers.py b/neural_lam/custom_loggers.py
index 5b6000e6..1d5ff9f3 100644
--- a/neural_lam/custom_loggers.py
+++ b/neural_lam/custom_loggers.py
@@ -2,7 +2,6 @@
 
 # Standard library
 import os
-from typing import Optional
 
 # Third-party
 import matplotlib.pyplot as plt
@@ -74,7 +73,7 @@ def log_image(
         self,
         key: str,
         images: list[plt.Figure],
-        step: Optional[int] = None,
+        step: int | None = None,
     ) -> None:
         """
         Log one or more matplotlib figures as images to MLFlow.
diff --git a/neural_lam/datastore/__init__.py b/neural_lam/datastore/__init__.py
index e2117217..06716ef9 100644
--- a/neural_lam/datastore/__init__.py
+++ b/neural_lam/datastore/__init__.py
@@ -1,5 +1,8 @@
 """Datastore backends for loading and serving weather model data."""
 
+# Standard library
+from pathlib import Path
+
 # Local
 from .base import BaseDatastore  # noqa
 from .mdp import MDPDatastore  # noqa
@@ -16,7 +19,9 @@
 }
 
 
-def init_datastore(datastore_kind, config_path):
+def init_datastore(
+    datastore_kind: str, config_path: str | Path
+) -> BaseDatastore:
     """
     Instantiate a datastore based on its short-name identifier.
 
diff --git a/neural_lam/datastore/base.py b/neural_lam/datastore/base.py
index 8376d38d..1abe3ac9 100644
--- a/neural_lam/datastore/base.py
+++ b/neural_lam/datastore/base.py
@@ -111,7 +111,7 @@ def get_vars_units(self, category: str) -> list[str]:
 
         Returns
         -------
-        List[str]
+        list[str]
             The units of the variables.
 
         """
@@ -127,7 +127,7 @@ def get_vars_names(self, category: str) -> list[str]:
 
         Returns
         -------
-        List[str]
+        list[str]
             The names of the variables.
 
         """
@@ -143,7 +143,7 @@ def get_vars_long_names(self, category: str) -> list[str]:
 
         Returns
         -------
-        List[str]
+        list[str]
             The long names of the variables.
 
         """
@@ -333,7 +333,7 @@ def get_xy_extent(self, category: str) -> list[float]:
 
         Returns
         -------
-        List[float]
+        list[float]
             The extent of the x, y coordinates.
 
         """
@@ -423,7 +423,7 @@ def expected_dim_order(
             The category of the dataset (state/forcing/static).
         Returns
         -------
-        List[str]
+        list[str]
             The expected dimension order for the dataarray or dataset.
 
         """
@@ -496,7 +496,7 @@ class BaseRegularGridDatastore(BaseDatastore):
     `stack_grid_coords` and `unstack_grid_coords` respectively).
     """
 
-    spatial_coordinates = ("x", "y")
+    spatial_coordinates: tuple[str, str] = ("x", "y")
 
     @cached_property
     @abc.abstractmethod
@@ -562,7 +562,7 @@ def unstack_grid_coords(
         da_or_ds_unstacked = da_or_ds.unstack("grid_index")
 
         # Ensure that the x, y dimensions are in the correct order
-        dims = da_or_ds_unstacked.dims
+        dims = list(da_or_ds_unstacked.dims)
         xy_dim_order = [d for d in dims if d in self.spatial_coordinates]
 
         if xy_dim_order != self.spatial_coordinates:
@@ -615,7 +615,7 @@ def stack_grid_coords(
         # dimension named in the format `{category}_feature`
         category = None
         for dim in da_or_ds_stacked.dims:
-            if dim.endswith("_feature"):
+            if isinstance(dim, str) and dim.endswith("_feature"):
                 if category is not None:
                     raise ValueError(
                         "Multiple dimensions ending with '_feature' found in "
diff --git a/neural_lam/datastore/mdp.py b/neural_lam/datastore/mdp.py
index 7cad45d7..860f1af3 100644
--- a/neural_lam/datastore/mdp.py
+++ b/neural_lam/datastore/mdp.py
@@ -7,7 +7,6 @@
 from datetime import timedelta
 from functools import cached_property
 from pathlib import Path
-from typing import List, Optional, Union
 
 # Third-party
 import cartopy.crs as ccrs
@@ -36,7 +35,7 @@ class MDPDatastore(BaseRegularGridDatastore):
 
     def __init__(
         self,
-        config_path: Union[str, Path],
+        config_path: str | Path,
         n_boundary_points: int = 30,
         reuse_existing: bool = True,
     ) -> None:
@@ -176,7 +175,7 @@ def step_length(self) -> timedelta:
         total_sec = da_dt.dt.total_seconds().isel(time=0).astype(int)
         return timedelta(seconds=int(total_sec.item()))
 
-    def get_vars_units(self, category: str) -> List[str]:
+    def get_vars_units(self, category: str) -> list[str]:
         """Return the units of the variables in the given category.
 
         Parameters
@@ -186,7 +185,7 @@ def get_vars_units(self, category: str) -> List[str]:
 
         Returns
         -------
-        List[str]
+        list[str]
             The units of the variables in the given category.
 
         """
@@ -195,7 +194,7 @@ def get_vars_units(self, category: str) -> List[str]:
             return []
         return self._ds[f"{category}_feature_units"].values.tolist()
 
-    def get_vars_names(self, category: str) -> List[str]:
+    def get_vars_names(self, category: str) -> list[str]:
         """Return the names of the variables in the given category.
 
         Parameters
@@ -205,7 +204,7 @@ def get_vars_names(self, category: str) -> List[str]:
 
         Returns
         -------
-        List[str]
+        list[str]
             The names of the variables in the given category.
 
         """
@@ -214,7 +213,7 @@ def get_vars_names(self, category: str) -> List[str]:
             return []
         return self._ds[f"{category}_feature"].values.tolist()
 
-    def get_vars_long_names(self, category: str) -> List[str]:
+    def get_vars_long_names(self, category: str) -> list[str]:
         """
         Return the long names of the variables in the given category.
 
@@ -225,7 +224,7 @@ def get_vars_long_names(self, category: str) -> List[str]:
 
         Returns
         -------
-        List[str]
+        list[str]
             The long names of the variables in the given category.
 
         """
@@ -253,9 +252,9 @@ def get_num_data_vars(self, category: str) -> int:
     def get_dataarray(
         self,
         category: str,
-        split: Optional[str],
+        split: str | None,
         standardize: bool = False,
-    ) -> Union[xr.DataArray, None]:
+    ) -> xr.DataArray | None:
         """
         Return the processed data (as a single `xr.DataArray`) for the given
         category of data and test/train/val-split that covers all the data (in
@@ -364,7 +363,7 @@ def get_standardization_dataarray(self, category: str) -> xr.Dataset:
         # Add standardized state diff stats
         if category == "state":
             ds_stats = ds_stats.assign(
-                **{
+                {
                     f"state_diff_{op}_standardized": self._ds[
                         f"state__{split}__diff_{op}"
                     ]
@@ -404,7 +403,11 @@ def boundary_mask(self) -> xr.DataArray:
         ds_unstacked["boundary_mask"] = ds_unstacked.boundary_mask.fillna(
             1
         ).astype(int)
-        return self.stack_grid_coords(da_or_ds=ds_unstacked.boundary_mask)
+        boundary_mask = self.stack_grid_coords(
+            da_or_ds=ds_unstacked.boundary_mask
+        )
+        assert isinstance(boundary_mask, xr.DataArray)
+        return boundary_mask
 
     @property
     def coords_projection(self) -> ccrs.Projection:
diff --git a/neural_lam/datastore/npyfilesmeps/compute_standardization_stats.py b/neural_lam/datastore/npyfilesmeps/compute_standardization_stats.py
index 9e694f14..2c8719dd 100644
--- a/neural_lam/datastore/npyfilesmeps/compute_standardization_stats.py
+++ b/neural_lam/datastore/npyfilesmeps/compute_standardization_stats.py
@@ -6,6 +6,7 @@
 from argparse import ArgumentParser
 from datetime import timedelta
 from pathlib import Path
+from typing import Any, cast
 
 # Third-party
 import torch
@@ -22,7 +23,12 @@
 class PaddedWeatherDataset(torch.utils.data.Dataset):
     """Wrap :class:`WeatherDataset` to pad samples for distributed runners."""
 
-    def __init__(self, base_dataset, world_size, batch_size):
+    def __init__(
+        self,
+        base_dataset: WeatherDataset,
+        world_size: int,
+        batch_size: int,
+    ) -> None:
         """
         Parameters
         ----------
@@ -46,7 +52,9 @@ def __init__(self, base_dataset, world_size, batch_size):
             range(self.total_samples, self.total_samples + self.padded_samples)
         )
 
-    def __getitem__(self, idx):
+    def __getitem__(
+        self, idx: int
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         """Return an item, repeating the final sample for padded indices."""
         return self.base_dataset[
             (
@@ -56,16 +64,16 @@ def __getitem__(self, idx):
             )
         ]
 
-    def __len__(self):
+    def __len__(self) -> int:
         """Return the padded dataset length."""
         return self.total_samples + self.padded_samples
 
-    def get_original_indices(self):
+    def get_original_indices(self) -> list[int]:
         """Return indices of the non-padded samples."""
         return self.original_indices
 
 
-def get_rank():
+def get_rank() -> int:
     """
     Return the rank inferred from SLURM or default to 0.
 
@@ -77,7 +85,7 @@ def get_rank():
     return int(os.environ.get("SLURM_PROCID", 0))
 
 
-def get_world_size():
+def get_world_size() -> int:
     """
     Return the world size inferred from SLURM or default to 1.
 
@@ -89,7 +97,9 @@ def get_world_size():
     return int(os.environ.get("SLURM_NTASKS", 1))
 
 
-def setup(rank, world_size):  # pylint: disable=redefined-outer-name
+def setup(
+    rank: int, world_size: int
+) -> None:  # pylint: disable=redefined-outer-name
     """
     Initialize the distributed group.
 
@@ -140,8 +150,13 @@ def setup(rank, world_size):  # pylint: disable=redefined-outer-name
 
 
 def save_stats(
-    static_dir_path, means, squares, flux_means, flux_squares, filename_prefix
-):
+    static_dir_path: str | Path,
+    means: list[torch.Tensor],
+    squares: list[torch.Tensor],
+    flux_means: list[torch.Tensor],
+    flux_squares: list[torch.Tensor],
+    filename_prefix: str,
+) -> None:
     """
     Aggregate running statistics and persist them to ``static_dir_path``.
 
@@ -174,14 +189,14 @@ def save_stats(
     filename_prefix : str
         Prefix (e.g., ``"parameter"`` or ``"diff"``) for saved tensors.
     """
-    means = (
-        torch.stack(means) if len(means) > 1 else means[0]
+    means_tensor = (
+        torch.stack(list(means)) if len(means) > 1 else means[0]
     )  # (B, d_features,)
-    squares = (
-        torch.stack(squares) if len(squares) > 1 else squares[0]
+    squares_tensor = (
+        torch.stack(list(squares)) if len(squares) > 1 else squares[0]
     )  # (B, d_features,)
-    mean = torch.mean(means, dim=0)  # (d_features,)
-    second_moment = torch.mean(squares, dim=0)  # (d_features,)
+    mean = torch.mean(means_tensor, dim=0)  # (d_features,)
+    second_moment = torch.mean(squares_tensor, dim=0)  # (d_features,)
     std = torch.sqrt(second_moment - mean**2)  # (d_features,)
     print(
         f"Saving {filename_prefix} mean and std.-dev. to "
@@ -196,14 +211,16 @@ def save_stats(
 
     if len(flux_means) == 0:
         return
-    flux_means = (
-        torch.stack(flux_means) if len(flux_means) > 1 else flux_means[0]
+    flux_means_tensor = (
+        torch.stack(list(flux_means)) if len(flux_means) > 1 else flux_means[0]
     )  # (B,)
-    flux_squares = (
-        torch.stack(flux_squares) if len(flux_squares) > 1 else flux_squares[0]
+    flux_squares_tensor = (
+        torch.stack(list(flux_squares))
+        if len(flux_squares) > 1
+        else flux_squares[0]
     )  # (B,)
-    flux_mean = torch.mean(flux_means)  # (,)
-    flux_second_moment = torch.mean(flux_squares)  # (,)
+    flux_mean = torch.mean(flux_means_tensor)  # (,)
+    flux_second_moment = torch.mean(flux_squares_tensor)  # (,)
     flux_std = torch.sqrt(flux_second_moment - flux_mean**2)  # (,)
     print("Saving flux mean and std.-dev. to flux_stats.pt")
     torch.save(
@@ -213,8 +230,12 @@ def save_stats(
 
 
 def main(
-    datastore_config_path, batch_size, step_length, n_workers, distributed
-):
+    datastore_config_path: str | Path,
+    batch_size: int,
+    step_length: timedelta,
+    n_workers: int,
+    distributed: bool,
+) -> None:
     """
     Pre-compute and persist standardization statistics from the datastore.
 
@@ -252,16 +273,18 @@ def main(
     # 65 forecast steps - 2 initial steps = 63
     ar_steps = 63
     # Raw (non-standardized) data for computing mean/std
-    ds = WeatherDataset(
+    ds_base = WeatherDataset(
         datastore=datastore,
         split="train",
         ar_steps=ar_steps,
         num_past_forcing_steps=0,
         num_future_forcing_steps=0,
     )
+    sampler: DistributedSampler | None
+    ds: torch.utils.data.Dataset
     if distributed:
         ds = PaddedWeatherDataset(
-            ds,
+            ds_base,
             world_size,
             batch_size,
         )
@@ -269,6 +292,7 @@ def main(
             ds, num_replicas=world_size, rank=rank, shuffle=False
         )
     else:
+        ds = ds_base
         sampler = None
     loader = torch.utils.data.DataLoader(
         ds,
@@ -302,33 +326,33 @@ def main(
         flux_squares.append(torch.mean(flux_batch**2).cpu())  # (,)
 
     if distributed and world_size > 1:
-        means_gathered, squares_gathered = [None] * world_size, [
-            None
-        ] * world_size
-        flux_means_gathered, flux_squares_gathered = (
-            [None] * world_size,
-            [None] * world_size,
-        )
+        means_gathered: list[Any] = [None] * world_size
+        squares_gathered: list[Any] = [None] * world_size
+        flux_means_gathered: list[Any] = [None] * world_size
+        flux_squares_gathered: list[Any] = [None] * world_size
         dist.all_gather_object(means_gathered, torch.cat(means, dim=0))
         dist.all_gather_object(squares_gathered, torch.cat(squares, dim=0))
         dist.all_gather_object(flux_means_gathered, flux_means)
         dist.all_gather_object(flux_squares_gathered, flux_squares)
 
         if rank == 0:
-            means_gathered, squares_gathered = (
-                torch.cat(means_gathered, dim=0),
-                torch.cat(squares_gathered, dim=0),
+            means_gathered_tensor = torch.cat(
+                cast(list[torch.Tensor], means_gathered), dim=0
+            )
+            squares_gathered_tensor = torch.cat(
+                cast(list[torch.Tensor], squares_gathered), dim=0
             )
 
+            assert isinstance(ds, PaddedWeatherDataset)
             original_indices = ds.get_original_indices()
             means, squares = (
-                [means_gathered[i] for i in original_indices],
-                [squares_gathered[i] for i in original_indices],
+                [means_gathered_tensor[i] for i in original_indices],
+                [squares_gathered_tensor[i] for i in original_indices],
             )
             flux_means = [
                 torch.cat(
                     [
-                        torch.stack(rank_flux)
+                        torch.stack(cast(list[torch.Tensor], rank_flux))
                         for rank_flux in flux_means_gathered
                     ]
                 )
@@ -336,7 +360,7 @@ def main(
             flux_squares = [
                 torch.cat(
                     [
-                        torch.stack(rank_flux)
+                        torch.stack(cast(list[torch.Tensor], rank_flux))
                         for rank_flux in flux_squares_gathered
                     ]
                 )
@@ -362,16 +386,18 @@ def main(
 
     if rank == 0:
         print("Computing mean and std.-dev. for one-step differences...")
-    ds_standard = WeatherDataset(
+    ds_standard_base = WeatherDataset(
         datastore=datastore,
         split="train",
         ar_steps=ar_steps,
         num_past_forcing_steps=0,
         num_future_forcing_steps=0,
     )
+    sampler_standard: DistributedSampler | None
+    ds_standard: torch.utils.data.Dataset
     if distributed:
         ds_standard = PaddedWeatherDataset(
-            ds_standard,
+            ds_standard_base,
             world_size,
             batch_size,
         )
@@ -379,6 +405,7 @@ def main(
             ds_standard, num_replicas=world_size, rank=rank, shuffle=False
         )
     else:
+        ds_standard = ds_standard_base
         sampler_standard = None
     loader_standard = torch.utils.data.DataLoader(
         ds_standard,
@@ -438,10 +465,8 @@ def main(
 
     if distributed and world_size > 1:
         dist.barrier()
-        diff_means_gathered, diff_squares_gathered = (
-            [None] * world_size,
-            [None] * world_size,
-        )
+        diff_means_gathered: list[Any] = [None] * world_size
+        diff_squares_gathered: list[Any] = [None] * world_size
         dist.all_gather_object(
             diff_means_gathered, torch.cat(diff_means, dim=0)
         )
@@ -450,13 +475,18 @@ def main(
         )
 
         if rank == 0:
-            diff_means_gathered = torch.cat(diff_means_gathered, dim=0)
-            diff_squares_gathered = torch.cat(diff_squares_gathered, dim=0)
+            diff_means_gathered_tensor = torch.cat(
+                cast(list[torch.Tensor], diff_means_gathered), dim=0
+            )
+            diff_squares_gathered_tensor = torch.cat(
+                cast(list[torch.Tensor], diff_squares_gathered), dim=0
+            )
+            assert isinstance(ds_standard, PaddedWeatherDataset)
             n_original_windows = (
                 len(ds_standard.get_original_indices()) * time_step_int
             )
-            diff_means = [diff_means_gathered[:n_original_windows]]
-            diff_squares = [diff_squares_gathered[:n_original_windows]]
+            diff_means = [diff_means_gathered_tensor[:n_original_windows]]
+            diff_squares = [diff_squares_gathered_tensor[:n_original_windows]]
 
     diff_means = [torch.cat(diff_means, dim=0)]  # (B', d_features,)
     diff_squares = [torch.cat(diff_squares, dim=0)]  # (B', d_features,)
@@ -468,7 +498,7 @@ def main(
         dist.destroy_process_group()
 
 
-def cli():
+def cli() -> None:
     """Parse CLI arguments and trigger :func:`main`."""
     parser = ArgumentParser(description="Training arguments")
     parser.add_argument(
diff --git a/neural_lam/datastore/npyfilesmeps/config.py b/neural_lam/datastore/npyfilesmeps/config.py
index b423b4a1..be55dc73 100644
--- a/neural_lam/datastore/npyfilesmeps/config.py
+++ b/neural_lam/datastore/npyfilesmeps/config.py
@@ -3,7 +3,7 @@
 # Standard library
 from dataclasses import dataclass, field
 from datetime import timedelta
-from typing import Any, Dict, List
+from typing import Any
 
 # Third-party
 import dataclass_wizard
@@ -24,7 +24,7 @@ class Projection:
     """
 
     class_name: str
-    kwargs: Dict[str, Any]
+    kwargs: dict[str, Any]
 
 
 @dataclass
@@ -47,14 +47,14 @@ class Dataset:
     """
 
     name: str
-    var_names: List[str]
-    var_units: List[str]
-    var_longnames: List[str]
+    var_names: list[str]
+    var_units: list[str]
+    var_longnames: list[str]
     num_forcing_features: int
     num_timesteps: int
     step_length: timedelta
     num_ensemble_members: int
-    remove_state_features_with_index: List[int] = field(default_factory=list)
+    remove_state_features_with_index: list[int] = field(default_factory=list)
 
 
 @dataclass
@@ -70,5 +70,5 @@ class NpyDatastoreConfig(dataclass_wizard.YAMLWizard):
     """
 
     dataset: Dataset
-    grid_shape_state: List[int]
+    grid_shape_state: list[int]
     projection: Projection
diff --git a/neural_lam/datastore/npyfilesmeps/store.py b/neural_lam/datastore/npyfilesmeps/store.py
index e14bbc74..343a067a 100644
--- a/neural_lam/datastore/npyfilesmeps/store.py
+++ b/neural_lam/datastore/npyfilesmeps/store.py
@@ -10,7 +10,7 @@
 from datetime import timedelta
 from functools import cached_property
 from pathlib import Path
-from typing import List, Optional
+from typing import Any, cast
 
 # Third-party
 import cartopy.crs as ccrs
@@ -34,7 +34,11 @@
 OPEN_WATER_FILENAME_FORMAT = "wtr_{analysis_time:%Y%m%d%H}.npy"
 
 
-def _load_np(fp, add_feature_dim, feature_dim_mask=None):
+def _load_np(
+    fp: str | Path,
+    add_feature_dim: bool,
+    feature_dim_mask: np.ndarray | None = None,
+) -> np.ndarray:
     """
     Load an ``.npy`` file and optionally expand/mask the feature axis.
 
@@ -162,10 +166,18 @@ class NpyFilesDatastoreMEPS(BaseRegularGridDatastore):
 
     is_forecast = True
 
+    _config_path: Path
+    _root_path: Path
+    _config: NpyDatastoreConfig
+    _num_ensemble_members: int
+    _num_timesteps: int
+    _step_length: timedelta
+    _remove_state_features_with_index: list[int] | None
+
     def __init__(
         self,
-        config_path,
-    ):
+        config_path: str | Path,
+    ) -> None:
         """
         Create a new NpyFilesDatastore using the configuration file at the
         given path. The config file should be a YAML file and will be loaded
@@ -220,7 +232,7 @@ def config(self) -> NpyDatastoreConfig:
         return self._config
 
     def get_dataarray(
-        self, category: str, split: Optional[str], standardize: bool = False
+        self, category: str, split: str | None, standardize: bool = False
     ) -> DataArray:
         """
         Get the data array for the given category and split of data. If the
@@ -314,7 +326,9 @@ def get_dataarray(
         da = da.rename(dict(feature=f"{category}_feature"))
 
         # stack the [x, y] dimensions into a `grid_index` dimension
-        da = self.stack_grid_coords(da)
+        da_stacked = self.stack_grid_coords(da)
+        assert isinstance(da_stacked, xr.DataArray)
+        da = da_stacked
 
         # check that we have the right features
         actual_features = da[f"{category}_feature"].values.tolist()
@@ -334,9 +348,9 @@ def get_dataarray(
 
     def _get_single_timeseries_dataarray(
         self,
-        features: List[str],
-        split: Optional[str] = None,
-        member: Optional[int] = None,
+        features: list[str],
+        split: str | None = None,
+        member: int | None = None,
     ) -> DataArray:
         """
         Get the data array spanning the complete time series for a given set of
@@ -347,7 +361,7 @@ def _get_single_timeseries_dataarray(
 
         Parameters
         ----------
-        features : List[str]
+        features : list[str]
             The list of features to load the data for. For the 'state'
             category, this should be the result of
             `self.get_vars_names(category="state")`, for the 'forcing' category
@@ -473,9 +487,11 @@ def _get_single_timeseries_dataarray(
         x = xs[:, 0]  # Unique x-coordinates (changes along the first axis)
         y = ys[0, :]  # Unique y-coordinates (changes along the second axis)
         for d in dims:
+            coord_values: Any
             if d == "elapsed_forecast_duration":
                 coord_values = self.step_length * np.arange(self._num_timesteps)
             elif d == "analysis_time":
+                assert split is not None
                 coord_values = self._get_analysis_times(split=split)
             elif d == "y":
                 coord_values = y
@@ -536,7 +552,7 @@ def _get_single_timeseries_dataarray(
 
         return da
 
-    def _get_analysis_times(self, split) -> List[np.datetime64]:
+    def _get_analysis_times(self, split: str) -> list[np.datetime64]:
         """Get the analysis times for the given split by parsing the filenames
         of all the files found for the given split.
 
@@ -547,7 +563,7 @@ def _get_analysis_times(self, split) -> List[np.datetime64]:
 
         Returns
         -------
-        List[dt.datetime]
+        list[dt.datetime]
             The analysis times for the given split, sorted in ascending order.
 
         """
@@ -568,7 +584,9 @@ def _get_analysis_times(self, split) -> List[np.datetime64]:
 
         return sorted(times)
 
-    def _calc_datetime_forcing_features(self, da_time: xr.DataArray):
+    def _calc_datetime_forcing_features(
+        self, da_time: xr.DataArray
+    ) -> xr.DataArray:
         """
         Compute sinusoidal encodings of hour-of-day and day-of-year.
 
@@ -586,11 +604,14 @@ def _calc_datetime_forcing_features(self, da_time: xr.DataArray):
         da_year_angle = da_time.dt.dayofyear / 365 * 2 * np.pi
 
         da_datetime_forcing = xr.concat(
-            (
-                np.sin(da_hour_angle),
-                np.cos(da_hour_angle),
-                np.sin(da_year_angle),
-                np.cos(da_year_angle),
+            cast(
+                list[xr.DataArray],
+                [
+                    np.sin(da_hour_angle),
+                    np.cos(da_hour_angle),
+                    np.sin(da_year_angle),
+                    np.cos(da_year_angle),
+                ],
             ),
             dim="feature",
         )
@@ -604,7 +625,7 @@ def _calc_datetime_forcing_features(self, da_time: xr.DataArray):
 
         return da_datetime_forcing
 
-    def get_vars_units(self, category: str) -> List[str]:
+    def get_vars_units(self, category: str) -> list[str]:
         """Return unit strings for the variables in ``category``."""
         if category == "state":
             return self.config.dataset.var_units
@@ -622,7 +643,7 @@ def get_vars_units(self, category: str) -> List[str]:
         else:
             raise NotImplementedError(f"Category {category} not supported")
 
-    def get_vars_names(self, category: str) -> List[str]:
+    def get_vars_names(self, category: str) -> list[str]:
         """Return canonical short names for the variables in ``category``."""
         if category == "state":
             return self.config.dataset.var_names
@@ -642,7 +663,7 @@ def get_vars_names(self, category: str) -> List[str]:
         else:
             raise NotImplementedError(f"Category {category} not supported")
 
-    def get_vars_long_names(self, category: str) -> List[str]:
+    def get_vars_long_names(self, category: str) -> list[str]:
         """Return descriptive names for the variables in ``category``."""
         if category == "state":
             return self.config.dataset.var_longnames
@@ -742,6 +763,7 @@ def boundary_mask(self) -> xr.DataArray:
             values, dims=["y", "x"], coords=dict(x=x, y=y), name="boundary_mask"
         )
         da_mask_stacked_xy = self.stack_grid_coords(da_mask).astype(int)
+        assert isinstance(da_mask_stacked_xy, xr.DataArray)
         return da_mask_stacked_xy
 
     def get_standardization_dataarray(self, category: str) -> xr.Dataset:
@@ -766,7 +788,7 @@ def get_standardization_dataarray(self, category: str) -> xr.Dataset:
 
         """
 
-        def load_pickled_tensor(fn):
+        def load_pickled_tensor(fn: str) -> np.ndarray:
             """Load a serialized tensor from ``static`` and convert to numpy."""
             return torch.load(
                 self.root_path / "static" / fn, weights_only=True
diff --git a/neural_lam/datastore/plot_example.py b/neural_lam/datastore/plot_example.py
index 13f32e57..d14bad2e 100644
--- a/neural_lam/datastore/plot_example.py
+++ b/neural_lam/datastore/plot_example.py
@@ -1,21 +1,24 @@
 """CLI helper to plot slices from datastores for manual inspection."""
 
 # Third-party
+import matplotlib.figure
 import matplotlib.pyplot as plt
+import xarray as xr
 
 # Local
 from . import DATASTORES, init_datastore
+from .base import BaseRegularGridDatastore
 
 
 def plot_example_from_datastore(
-    category,
-    datastore,
-    col_dim,
-    split="train",
-    standardize=True,
-    selection={},
-    index_selection={},
-):
+    category: str,
+    datastore: BaseRegularGridDatastore,
+    col_dim: str,
+    split: str = "train",
+    standardize: bool = True,
+    selection: dict = {},
+    index_selection: dict = {},
+) -> matplotlib.figure.Figure:
     """
     Create a plot of the data from the datastore.
 
@@ -47,10 +50,13 @@ def plot_example_from_datastore(
         Matplotlib figure object.
     """
     da = datastore.get_dataarray(category=category, split=split)
+    assert da is not None
     if standardize:
         da_stats = datastore.get_standardization_dataarray(category=category)
         da = (da - da_stats[f"{category}_mean"]) / da_stats[f"{category}_std"]
-    da = datastore.unstack_grid_coords(da)
+    da_unstacked = datastore.unstack_grid_coords(da)
+    assert isinstance(da_unstacked, xr.DataArray)
+    da = da_unstacked
 
     if len(selection) > 0:
         da = da.sel(**selection)
@@ -58,10 +64,10 @@ def plot_example_from_datastore(
         da = da.isel(**index_selection)
 
     col = col_dim.format(category=category)
-
     # check that the column dimension exists and that the resulting shape is 2D
     if col not in da.dims:
         raise ValueError(f"Column dimension {col} not found in dataarray.")
+
     da_col_item = da.isel({col: 0}).squeeze()
     if not len(da_col_item.shape) == 2:
         raise ValueError(
@@ -94,16 +100,17 @@ def plot_example_from_datastore(
     # Standard library
     import argparse
 
-    def _parse_dict(arg_str):
+    def _parse_dict(arg_str: str) -> tuple[str, int | float | str]:
         """Parse ``key=value`` CLI arguments into typed dictionary entries."""
         key, value = arg_str.split("=")
+        typed_value: int | float | str = value
         for op in [int, float]:
             try:
-                value = op(value)
+                typed_value = op(value)
                 break
             except ValueError:
                 pass
-        return key, value
+        return key, typed_value
 
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -180,6 +187,10 @@ def _parse_dict(arg_str):
         datastore_kind=datastore_kind,
         config_path=args.datastore_config_path,
     )
+    # Standard library
+    from typing import cast
+
+    datastore = cast(BaseRegularGridDatastore, datastore)
 
     plot_example_from_datastore(
         args.category,
diff --git a/neural_lam/gnn_layers.py b/neural_lam/gnn_layers.py
index 7a92ea76..f61f3029 100644
--- a/neural_lam/gnn_layers.py
+++ b/neural_lam/gnn_layers.py
@@ -1,7 +1,6 @@
 """Interaction Network and PropagationNet GNN layers used by Neural-LAM."""
 
 # Standard library
-from typing import Optional, Type, Union
 
 # Third-party
 import torch
@@ -27,9 +26,9 @@ def __init__(
         input_dim: int,
         update_edges: bool = True,
         hidden_layers: int = 1,
-        hidden_dim: Optional[int] = None,
-        edge_chunk_sizes: Optional[list[int]] = None,
-        aggr_chunk_sizes: Optional[list[int]] = None,
+        hidden_dim: int | None = None,
+        edge_chunk_sizes: list[int] | None = None,
+        aggr_chunk_sizes: list[int] | None = None,
         aggr: str = "sum",
     ) -> None:
         """
@@ -113,7 +112,7 @@ def forward(
         send_rep: torch.Tensor,
         rec_rep: torch.Tensor,
         edge_rep: torch.Tensor,
-    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         """
         Apply the interaction network to update receiver node
         representations, and optionally edge representations.
@@ -177,8 +176,8 @@ def aggregate(
         self,
         inputs: torch.Tensor,
         index: torch.Tensor,
-        ptr: Optional[torch.Tensor],
-        dim_size: Optional[int],
+        ptr: torch.Tensor | None,
+        dim_size: int | None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Overridden aggregation function to:
@@ -205,9 +204,9 @@ def __init__(
         input_dim: int,
         update_edges: bool = True,
         hidden_layers: int = 1,
-        hidden_dim: Optional[int] = None,
-        edge_chunk_sizes: Optional[list[int]] = None,
-        aggr_chunk_sizes: Optional[list[int]] = None,
+        hidden_dim: int | None = None,
+        edge_chunk_sizes: list[int] | None = None,
+        aggr_chunk_sizes: list[int] | None = None,
         aggr: str = "sum",
     ) -> None:
         """Initialise the :class:`PropagationNet` layer.
@@ -256,7 +255,7 @@ def message(
 }
 
 
-def get_gnn_class(gnn_type: str) -> Type[pyg.nn.MessagePassing]:
+def get_gnn_class(gnn_type: str) -> type[pyg.nn.MessagePassing]:
     """
     Look up a GNN class by name.
 
diff --git a/neural_lam/metrics.py b/neural_lam/metrics.py
index 1eb1d526..c332f011 100644
--- a/neural_lam/metrics.py
+++ b/neural_lam/metrics.py
@@ -2,7 +2,6 @@
 
 # Standard library
 from collections.abc import Callable
-from typing import Optional
 
 # Third-party
 import torch
@@ -37,7 +36,7 @@ def get_metric(metric_name: str) -> Callable[..., torch.Tensor]:
 
 def mask_and_reduce_metric(
     metric_entry_vals: torch.Tensor,
-    mask: Optional[torch.Tensor],
+    mask: torch.Tensor | None,
     average_grid: bool,
     sum_vars: bool,
 ) -> torch.Tensor:
@@ -89,7 +88,7 @@ def wmse(
     pred: torch.Tensor,
     target: torch.Tensor,
     pred_std: torch.Tensor,
-    mask: Optional[torch.Tensor] = None,
+    mask: torch.Tensor | None = None,
     average_grid: bool = True,
     sum_vars: bool = True,
 ) -> torch.Tensor:
@@ -142,7 +141,7 @@ def mse(
     pred: torch.Tensor,
     target: torch.Tensor,
     pred_std: torch.Tensor,
-    mask: Optional[torch.Tensor] = None,
+    mask: torch.Tensor | None = None,
     average_grid: bool = True,
     sum_vars: bool = True,
 ) -> torch.Tensor:
@@ -187,7 +186,7 @@ def wmae(
     pred: torch.Tensor,
     target: torch.Tensor,
     pred_std: torch.Tensor,
-    mask: Optional[torch.Tensor] = None,
+    mask: torch.Tensor | None = None,
     average_grid: bool = True,
     sum_vars: bool = True,
 ) -> torch.Tensor:
@@ -240,7 +239,7 @@ def mae(
     pred: torch.Tensor,
     target: torch.Tensor,
     pred_std: torch.Tensor,
-    mask: Optional[torch.Tensor] = None,
+    mask: torch.Tensor | None = None,
     average_grid: bool = True,
     sum_vars: bool = True,
 ) -> torch.Tensor:
@@ -285,7 +284,7 @@ def nll(
     pred: torch.Tensor,
     target: torch.Tensor,
     pred_std: torch.Tensor,
-    mask: Optional[torch.Tensor] = None,
+    mask: torch.Tensor | None = None,
     average_grid: bool = True,
     sum_vars: bool = True,
 ) -> torch.Tensor:
@@ -334,7 +333,7 @@ def crps_gauss(
     pred: torch.Tensor,
     target: torch.Tensor,
     pred_std: torch.Tensor,
-    mask: Optional[torch.Tensor] = None,
+    mask: torch.Tensor | None = None,
     average_grid: bool = True,
     sum_vars: bool = True,
 ) -> torch.Tensor:
diff --git a/neural_lam/models/forecasters/autoregressive.py b/neural_lam/models/forecasters/autoregressive.py
index a8135f62..7167c64c 100644
--- a/neural_lam/models/forecasters/autoregressive.py
+++ b/neural_lam/models/forecasters/autoregressive.py
@@ -17,6 +17,9 @@ class ARForecaster(Forecaster):
     unroll a forecast. Makes use of a StepPredictor at each AR step.
     """
 
+    boundary_mask: torch.Tensor
+    interior_mask: torch.Tensor
+
     def __init__(
         self, predictor: StepPredictor, datastore: BaseDatastore
     ) -> None:
diff --git a/neural_lam/models/module.py b/neural_lam/models/module.py
index 71ce7951..f8616bbc 100644
--- a/neural_lam/models/module.py
+++ b/neural_lam/models/module.py
@@ -19,7 +19,7 @@
 # Local
 from .. import metrics, vis
 from ..config import NeuralLAMConfig
-from ..datastore import BaseDatastore
+from ..datastore.base import BaseRegularGridDatastore
 from ..loss_weighting import get_state_feature_weighting
 from ..weather_dataset import WeatherDataset
 from .forecasters.base import Forecaster
@@ -33,11 +33,20 @@ class ForecasterModule(pl.LightningModule):
 
     # pylint: disable=arguments-differ
 
+    interior_mask_bool: torch.Tensor
+    per_var_std: torch.Tensor | None
+    state_mean: torch.Tensor
+    state_std: torch.Tensor
+    forcing_mean: torch.Tensor | None
+    forcing_std: torch.Tensor | None
+    forcing_mean_tiled: torch.Tensor | None
+    forcing_std_tiled: torch.Tensor | None
+
     def __init__(
         self,
         forecaster: Forecaster,
         config: NeuralLAMConfig,
-        datastore: BaseDatastore,
+        datastore: BaseRegularGridDatastore,
         loss: str = "wmse",
         lr: float = 1e-3,
         restore_opt: bool = False,
@@ -46,8 +55,8 @@ def __init__(
         val_steps_to_log: list[int] | None = None,
         metrics_watch: list[str] | None = None,
         var_leads_metrics_watch: dict[int, list[int]] | None = None,
-        args=None,
-    ):
+        args: Any | None = None,
+    ) -> None:
         """
         Initialize the ForecasterModule.
 
@@ -266,13 +275,13 @@ def _create_dataarray_from_tensor(
             The resulting xarray DataArray.
         """
         weather_dataset = WeatherDataset(datastore=self.datastore, split=split)
-        time = np.array(time.cpu(), dtype="datetime64[ns]")
+        time_np = np.array(time.cpu(), dtype="datetime64[ns]")
         da = weather_dataset.create_dataarray_from_tensor(
-            tensor=tensor, time=time, category=category
+            tensor=tensor, time=time_np, category=category
         )
         return da
 
-    def configure_optimizers(self):
+    def configure_optimizers(self) -> torch.optim.Optimizer:
         """
         Configure the optimizers and learning rate schedulers.
 
@@ -281,13 +290,14 @@ def configure_optimizers(self):
         torch.optim.Optimizer
             The configured optimizer.
         """
-        opt = torch.optim.AdamW(
-            self.parameters(), lr=self.hparams.lr, betas=(0.9, 0.95)
-        )
+        lr = self.hparams.lr  # type: ignore[attr-defined]
+        opt = torch.optim.AdamW(self.parameters(), lr=lr, betas=(0.9, 0.95))
         return opt
 
     @staticmethod
-    def _safe_std(std_values, eps, category):
+    def _safe_std(
+        std_values: np.ndarray | torch.Tensor, eps: float, category: str
+    ) -> torch.Tensor:
         """Build a float32 std tensor, clamping near-zero values to `eps`.
 
         Mirrors the previous CPU-side WeatherDataset behavior: features with
@@ -304,7 +314,11 @@ def _safe_std(std_values, eps, category):
             )
         return torch.clamp(std, min=eps)
 
-    def on_after_batch_transfer(self, batch, dataloader_idx):
+    def on_after_batch_transfer(
+        self,
+        batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
+        dataloader_idx: int,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         """Standardize a batch on-device after transfer to the accelerator.
 
         Lightning calls this for every train/val/test/predict batch.
@@ -317,6 +331,9 @@ def on_after_batch_transfer(self, batch, dataloader_idx):
         target_states = (target_states - self.state_mean) / self.state_std
 
         if forcing.shape[-1] > 0:
+            assert (
+                self.forcing_mean is not None and self.forcing_std is not None
+            )
             if self.forcing_mean_tiled is None:
                 # Forcing is (..., num_forcing_vars * window_size).
                 # WeatherDataset stacks (forcing_feature, window)
@@ -330,13 +347,20 @@ def on_after_batch_transfer(self, batch, dataloader_idx):
                 self.forcing_std_tiled = self.forcing_std.repeat_interleave(
                     window_size
                 )
+            assert (
+                self.forcing_mean_tiled is not None
+                and self.forcing_std_tiled is not None
+            )
             forcing = (
                 forcing - self.forcing_mean_tiled
             ) / self.forcing_std_tiled
 
         return init_states, target_states, forcing, batch_times
 
-    def common_step(self, batch):
+    def common_step(
+        self,
+        batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None, torch.Tensor]:
         """
         Perform a common prediction step for training, validation, and testing.
 
@@ -358,7 +382,10 @@ def common_step(self, batch):
         )
         return prediction, target_states, pred_std, batch_times
 
-    def training_step(self, batch):
+    def training_step(
+        self,
+        batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
+    ) -> torch.Tensor:
         """
         Perform a single training step.
 
@@ -396,7 +423,7 @@ def training_step(self, batch):
         )
         return batch_loss
 
-    def all_gather_cat(self, tensor_to_gather):
+    def all_gather_cat(self, tensor_to_gather: torch.Tensor) -> torch.Tensor:
         """
         Gather tensors from all GPUs and concatenate them.
 
@@ -411,6 +438,7 @@ def all_gather_cat(self, tensor_to_gather):
             The concatenated tensor from all processes.
         """
         gathered = self.all_gather(tensor_to_gather)
+        assert isinstance(gathered, torch.Tensor)
         # all_gather adds dim 0 only on multi-device; on single
         # device it returns the same tensor unchanged.
         if gathered.dim() > tensor_to_gather.dim():
@@ -423,7 +451,11 @@ def _warn_skipped_val_steps(self, pred_steps: int, phase: str) -> None:
         flag = f"_{phase}_steps_warn_issued"
         if getattr(self, flag):
             return
-        invalid = [s for s in self.hparams.val_steps_to_log if s > pred_steps]
+        invalid = [
+            s
+            for s in self.hparams.val_steps_to_log  # type: ignore[attr-defined]
+            if s > pred_steps
+        ]
         if invalid:
             warnings.warn(
                 f"val_steps_to_log contains steps {invalid} that exceed "
@@ -435,7 +467,11 @@ def _warn_skipped_val_steps(self, pred_steps: int, phase: str) -> None:
             )
         setattr(self, flag, True)
 
-    def validation_step(self, batch, batch_idx):
+    def validation_step(
+        self,
+        batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
+        batch_idx: int,
+    ) -> None:
         """
         Perform a single validation step.
 
@@ -449,6 +485,7 @@ def validation_step(self, batch, batch_idx):
         prediction, target_states, pred_std, _ = self.common_step(batch)
         if pred_std is None:
             pred_std = self.per_var_std
+        assert pred_std is not None
 
         time_step_loss = torch.mean(
             self.loss(
@@ -462,9 +499,13 @@ def validation_step(self, batch, batch_idx):
         mean_loss = torch.mean(time_step_loss)
         self._warn_skipped_val_steps(len(time_step_loss), "val")
 
+        hparams = self.hparams
+        val_steps_to_log = (
+            hparams.val_steps_to_log  # type: ignore[attr-defined]
+        )
         val_log_dict = {
             f"val_loss_unroll{step}": time_step_loss[step - 1]
-            for step in self.hparams.val_steps_to_log
+            for step in val_steps_to_log
             if step <= len(time_step_loss)
         }
         val_log_dict["val_mean_loss"] = mean_loss
@@ -485,15 +526,21 @@ def validation_step(self, batch, batch_idx):
         )
         self.val_metrics["mse"].append(entry_mses)
 
-    def on_validation_epoch_end(self):
+    def on_validation_epoch_end(self) -> None:
         """
         Perform actions at the end of the validation epoch.
         Aggregates and plots validation metrics.
         """
         self.aggregate_and_plot_metrics(self.val_metrics, prefix="val")
 
-        if self.trainer.is_global_zero and self.hparams.metrics_watch:
-            unmatched = set(self.hparams.metrics_watch) - self.matched_metrics
+        if (
+            self.trainer.is_global_zero
+            and self.hparams.metrics_watch  # type: ignore[attr-defined]
+        ):
+            unmatched = (
+                set(self.hparams.metrics_watch)  # type: ignore[attr-defined]
+                - self.matched_metrics
+            )
             if unmatched:
                 warnings.warn(
                     "The following metrics in --metrics_watch "
@@ -508,7 +555,11 @@ def on_validation_epoch_end(self):
             metric_list.clear()
 
     # pylint: disable-next=unused-argument
-    def test_step(self, batch, batch_idx):
+    def test_step(
+        self,
+        batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
+        batch_idx: int,
+    ) -> None:
         """
         Perform a single test step.
 
@@ -529,6 +580,7 @@ def test_step(self, batch, batch_idx):
 
         if pred_std is None:
             pred_std = self.per_var_std
+        assert pred_std is not None
 
         time_step_loss = torch.mean(
             self.loss(
@@ -542,9 +594,13 @@ def test_step(self, batch, batch_idx):
         mean_loss = torch.mean(time_step_loss)
         self._warn_skipped_val_steps(len(time_step_loss), "test")
 
+        hparams = self.hparams
+        val_steps_to_log = (
+            hparams.val_steps_to_log  # type: ignore[attr-defined]
+        )
         test_log_dict = {
             f"test_loss_unroll{step}": time_step_loss[step - 1]
-            for step in self.hparams.val_steps_to_log
+            for step in val_steps_to_log
             if step <= len(time_step_loss)
         }
         test_log_dict["test_mean_loss"] = mean_loss
@@ -575,7 +631,7 @@ def test_step(self, batch, batch_idx):
             :,
             [
                 step - 1
-                for step in self.hparams.val_steps_to_log
+                for step in val_steps_to_log
                 if step <= spatial_loss.shape[1]
             ],
         ]
@@ -585,19 +641,25 @@ def test_step(self, batch, batch_idx):
             self.trainer.is_global_zero
             and self.plotted_examples < self.n_example_pred
         ):
-            n_additional_examples = min(
+            n_examples = min(
                 prediction.shape[0],
                 self.n_example_pred - self.plotted_examples,
             )
 
             self.plot_examples(
                 batch,
-                n_additional_examples,
+                n_examples,
                 prediction=prediction,
                 split="test",
             )
 
-    def plot_examples(self, batch, n_examples, split, prediction):
+    def plot_examples(
+        self,
+        batch: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
+        n_examples: int,
+        split: str,
+        prediction: torch.Tensor,
+    ) -> None:
         """
         Plot example predictions.
 
@@ -673,7 +735,7 @@ def plot_examples(self, batch, n_examples, split, prediction):
 
             if self.create_gif:
                 plot_dir_path = os.path.join(
-                    self.logger.save_dir,
+                    self.logger.save_dir,  # type: ignore[union-attr]
                     f"example_plots_{example_i}",
                 )
                 os.makedirs(plot_dir_path, exist_ok=True)
@@ -715,7 +777,9 @@ def plot_examples(self, batch, n_examples, split, prediction):
                         key = f"{var_name}_example"
 
                     if hasattr(self.logger, "log_image"):
-                        self.logger.log_image(key=key, images=[fig], step=t_i)
+                        self.logger.log_image(  # type: ignore[union-attr]
+                            key=key, images=[fig], step=t_i
+                        )
                     else:
                         warnings.warn(
                             f"{self.logger} does not support image logging."
@@ -755,19 +819,21 @@ def plot_examples(self, batch, n_examples, split, prediction):
             torch.save(
                 pred_slice.cpu(),
                 os.path.join(
-                    self.logger.save_dir,
+                    self.logger.save_dir,  # type: ignore[union-attr]
                     f"example_pred_{self.plotted_examples}.pt",
                 ),
             )
             torch.save(
                 target_slice.cpu(),
                 os.path.join(
-                    self.logger.save_dir,
+                    self.logger.save_dir,  # type: ignore[union-attr]
                     f"example_target_{self.plotted_examples}.pt",
                 ),
             )
 
-    def create_metric_log_dict(self, metric_tensor, prefix, metric_name):
+    def create_metric_log_dict(
+        self, metric_tensor: torch.Tensor, prefix: str, metric_name: str
+    ) -> dict[str, Any]:
         """
         Create a dictionary of metrics to log.
 
@@ -785,7 +851,7 @@ def create_metric_log_dict(self, metric_tensor, prefix, metric_name):
         dict
             Dictionary of logged metrics and figures.
         """
-        log_dict = {}
+        log_dict: dict[str, Any] = {}
         metric_fig = vis.plot_error_heatmap(
             errors=metric_tensor,
             datastore=self.datastore,
@@ -795,21 +861,29 @@ def create_metric_log_dict(self, metric_tensor, prefix, metric_name):
 
         if prefix == "test":
             metric_fig.savefig(
-                os.path.join(self.logger.save_dir, f"{full_log_name}.pdf")
+                os.path.join(
+                    self.logger.save_dir,  # type: ignore[union-attr]
+                    f"{full_log_name}.pdf",
+                )
             )
             np.savetxt(
-                os.path.join(self.logger.save_dir, f"{full_log_name}.csv"),
+                os.path.join(
+                    self.logger.save_dir,  # type: ignore[union-attr]
+                    f"{full_log_name}.csv",
+                ),
                 metric_tensor.cpu().numpy(),
                 delimiter=",",
             )
 
         var_names = self.datastore.get_vars_names(category="state")
-        if full_log_name in self.hparams.metrics_watch:
+        hparams = self.hparams
+        metrics_watch = hparams.metrics_watch  # type: ignore[attr-defined]
+        if full_log_name in metrics_watch:
             self.matched_metrics.add(full_log_name)
-            for (
-                var_i,
-                timesteps,
-            ) in self.hparams.var_leads_metrics_watch.items():
+            var_leads_metrics_watch = (
+                hparams.var_leads_metrics_watch  # type: ignore[attr-defined]
+            )
+            for var_i, timesteps in var_leads_metrics_watch.items():
                 var_name = var_names[var_i]
                 for step in timesteps:
                     key = f"{full_log_name}_{var_name}_step_{step}"
@@ -817,7 +891,9 @@ def create_metric_log_dict(self, metric_tensor, prefix, metric_name):
 
         return log_dict
 
-    def aggregate_and_plot_metrics(self, metrics_dict, prefix):
+    def aggregate_and_plot_metrics(
+        self, metrics_dict: dict[str, list[torch.Tensor]], prefix: str
+    ) -> None:
         """
         Aggregate metrics from all GPUs and plot them.
 
@@ -880,11 +956,13 @@ def aggregate_and_plot_metrics(self, metrics_dict, prefix):
                     key = f"{key}-{current_epoch}"
 
                 if hasattr(self.logger, "log_image"):
-                    self.logger.log_image(key=key, images=[figure])
+                    self.logger.log_image(  # type: ignore[union-attr]
+                        key=key, images=[figure]
+                    )
 
             plt.close("all")
 
-    def on_test_epoch_end(self):
+    def on_test_epoch_end(self) -> None:
         """
         Perform actions at the end of the test epoch.
         Aggregates and plots test metrics and spatial loss maps.
@@ -896,6 +974,11 @@ def on_test_epoch_end(self):
         )
         if self.trainer.is_global_zero:
             mean_spatial_loss = torch.mean(spatial_loss_tensor, dim=0)
+            hparams = self.hparams
+            val_steps_to_log = (
+                hparams.val_steps_to_log  # type: ignore[attr-defined]
+            )
+            logger_save_dir = self.logger.save_dir  # type: ignore[union-attr]
 
             loss_map_figs = [
                 vis.plot_spatial_error(
@@ -905,7 +988,8 @@ def on_test_epoch_end(self):
                     f"({(self.time_step_int * t_i)} {self.time_step_unit})",
                 )
                 for t_i, loss_map in zip(
-                    self.hparams.val_steps_to_log, mean_spatial_loss
+                    val_steps_to_log,
+                    mean_spatial_loss,
                 )
             ]
 
@@ -914,30 +998,35 @@ def on_test_epoch_end(self):
                 if not isinstance(self.logger, pl.loggers.WandbLogger):
                     key = f"{key}_{i}"
                 if hasattr(self.logger, "log_image"):
-                    self.logger.log_image(key=key, images=[fig])
+                    self.logger.log_image(  # type: ignore[union-attr]
+                        key=key, images=[fig]
+                    )
 
             pdf_loss_map_figs = [
                 vis.plot_spatial_error(error=loss_map, datastore=self.datastore)
                 for loss_map in mean_spatial_loss
             ]
             pdf_loss_maps_dir = os.path.join(
-                self.logger.save_dir, "spatial_loss_maps"
+                logger_save_dir, "spatial_loss_maps"
             )
             os.makedirs(pdf_loss_maps_dir, exist_ok=True)
             for t_i, fig in zip(
-                self.hparams.val_steps_to_log, pdf_loss_map_figs
+                val_steps_to_log,
+                pdf_loss_map_figs,
             ):
                 fig.savefig(os.path.join(pdf_loss_maps_dir, f"loss_t{t_i}.pdf"))
 
             torch.save(
                 mean_spatial_loss.cpu(),
-                os.path.join(self.logger.save_dir, "mean_spatial_loss.pt"),
+                os.path.join(
+                    logger_save_dir,
+                    "mean_spatial_loss.pt",
+                ),
             )
 
-            if self.hparams.metrics_watch:
-                unmatched = (
-                    set(self.hparams.metrics_watch) - self.matched_metrics
-                )
+            metrics_watch = hparams.metrics_watch  # type: ignore[attr-defined]
+            if metrics_watch:
+                unmatched = set(metrics_watch) - self.matched_metrics
                 if unmatched:
                     warnings.warn(
                         "The following metrics in --metrics_watch "
@@ -961,7 +1050,7 @@ def on_test_epoch_end(self):
         # otherwise stay permanently False).
         self.plotted_examples = 0
 
-    def on_load_checkpoint(self, checkpoint):
+    def on_load_checkpoint(self, checkpoint: dict[str, Any]) -> None:
         """
         Perform actions when loading a checkpoint.
         Handles backward compatibility for older checkpoints.
diff --git a/neural_lam/models/step_predictors/base.py b/neural_lam/models/step_predictors/base.py
index daf8b0e6..6efdaacf 100644
--- a/neural_lam/models/step_predictors/base.py
+++ b/neural_lam/models/step_predictors/base.py
@@ -18,6 +18,19 @@ class StepPredictor(nn.Module, ABC):
     time steps plus forcing into a prediction of the next state.
     """
 
+    grid_static_features: torch.Tensor
+    state_mean: torch.Tensor
+    state_std: torch.Tensor
+
+    # Registered buffers for clamping
+    sigmoid_lower_lims: torch.Tensor
+    sigmoid_upper_lims: torch.Tensor
+    softplus_lower_lims: torch.Tensor
+    softplus_upper_lims: torch.Tensor
+    clamp_lower_upper_idx: torch.Tensor
+    clamp_lower_idx: torch.Tensor
+    clamp_upper_idx: torch.Tensor
+
     def __init__(
         self,
         datastore: BaseDatastore,
diff --git a/neural_lam/models/step_predictors/graph/base.py b/neural_lam/models/step_predictors/graph/base.py
index 1a747971..5dc3ee0b 100644
--- a/neural_lam/models/step_predictors/graph/base.py
+++ b/neural_lam/models/step_predictors/graph/base.py
@@ -18,6 +18,16 @@ class BaseGraphModel(StepPredictor):
     the encode-process-decode idea.
     """
 
+    diff_mean: torch.Tensor
+    diff_std: torch.Tensor
+    g2m_features: torch.Tensor
+    m2g_features: torch.Tensor
+    g2m_edge_index: torch.Tensor
+    m2g_edge_index: torch.Tensor
+    mesh_static_features: torch.Tensor | list[torch.Tensor]
+    m2m_features: torch.Tensor | list[torch.Tensor]
+    m2m_edge_index: torch.Tensor | list[torch.Tensor]
+
     def __init__(
         self,
         datastore: BaseDatastore,
diff --git a/neural_lam/models/step_predictors/graph/graph_lam.py b/neural_lam/models/step_predictors/graph/graph_lam.py
index fdb12a12..1728307d 100644
--- a/neural_lam/models/step_predictors/graph/graph_lam.py
+++ b/neural_lam/models/step_predictors/graph/graph_lam.py
@@ -21,6 +21,10 @@ class GraphLAM(BaseGraphModel):
     Oskarsson et al. (2023).
     """
 
+    mesh_static_features: torch.Tensor
+    m2m_features: torch.Tensor
+    m2m_edge_index: torch.Tensor
+
     def __init__(
         self,
         datastore: BaseDatastore,
diff --git a/neural_lam/models/step_predictors/graph/hi_lam.py b/neural_lam/models/step_predictors/graph/hi_lam.py
index ee3eda8b..d01b56ac 100644
--- a/neural_lam/models/step_predictors/graph/hi_lam.py
+++ b/neural_lam/models/step_predictors/graph/hi_lam.py
@@ -5,6 +5,7 @@
 # Standard library
 
 # Third-party
+import torch
 from torch import nn
 
 # Local
@@ -37,7 +38,7 @@ def __init__(
         m2g_gnn_type: str = "InteractionNet",
         mesh_up_gnn_type: str = "InteractionNet",
         mesh_down_gnn_type: str = "InteractionNet",
-    ):
+    ) -> None:
         """
         Initialize the HiLAM model.
 
@@ -100,7 +101,7 @@ def __init__(
             [self.make_same_gnns() for _ in range(processor_layers)]
         )  # Nested lists (proc_steps, num_levels)
 
-    def make_same_gnns(self):
+    def make_same_gnns(self) -> nn.ModuleList:
         """
         Make intra-level GNNs.
 
@@ -120,7 +121,7 @@ def make_same_gnns(self):
             ]
         )
 
-    def make_up_gnns(self):
+    def make_up_gnns(self) -> nn.ModuleList:
         """
         Make GNNs for processing steps up through the hierarchy.
 
@@ -141,7 +142,7 @@ def make_up_gnns(self):
             ]
         )
 
-    def make_down_gnns(self):
+    def make_down_gnns(self) -> nn.ModuleList:
         """
         Make GNNs for processing steps down through the hierarchy.
 
@@ -164,12 +165,12 @@ def make_down_gnns(self):
 
     def mesh_down_step(
         self,
-        mesh_rep_levels,
-        mesh_same_rep,
-        mesh_down_rep,
-        down_gnns,
-        same_gnns,
-    ):
+        mesh_rep_levels: list[torch.Tensor],
+        mesh_same_rep: list[torch.Tensor],
+        mesh_down_rep: list[torch.Tensor],
+        down_gnns: nn.ModuleList,
+        same_gnns: nn.ModuleList,
+    ) -> tuple[list[torch.Tensor], list[torch.Tensor], list[torch.Tensor]]:
         """
         Run the downward pass of vertical processing, alternating between
         down-edges and same-level edges from the top to the bottom level.
@@ -234,8 +235,13 @@ def mesh_down_step(
         return mesh_rep_levels, mesh_same_rep, mesh_down_rep
 
     def mesh_up_step(
-        self, mesh_rep_levels, mesh_same_rep, mesh_up_rep, up_gnns, same_gnns
-    ):
+        self,
+        mesh_rep_levels: list[torch.Tensor],
+        mesh_same_rep: list[torch.Tensor],
+        mesh_up_rep: list[torch.Tensor],
+        up_gnns: nn.ModuleList,
+        same_gnns: nn.ModuleList,
+    ) -> tuple[list[torch.Tensor], list[torch.Tensor], list[torch.Tensor]]:
         """
         Run the upward pass of vertical processing, alternating between
         up-edges and same-level edges from the bottom to the top level.
@@ -300,8 +306,17 @@ def mesh_up_step(
         return mesh_rep_levels, mesh_same_rep, mesh_up_rep
 
     def hi_processor_step(
-        self, mesh_rep_levels, mesh_same_rep, mesh_up_rep, mesh_down_rep
-    ):
+        self,
+        mesh_rep_levels: list[torch.Tensor],
+        mesh_same_rep: list[torch.Tensor],
+        mesh_up_rep: list[torch.Tensor],
+        mesh_down_rep: list[torch.Tensor],
+    ) -> tuple[
+        list[torch.Tensor],
+        list[torch.Tensor],
+        list[torch.Tensor],
+        list[torch.Tensor],
+    ]:
         """
         Run all processor steps (down then up at each layer depth) over
         the hierarchical mesh.
@@ -337,6 +352,11 @@ def hi_processor_step(
             self.mesh_up_gnns,
             self.mesh_up_same_gnns,
         ):
+            assert isinstance(down_gnns, nn.ModuleList)
+            assert isinstance(down_same_gnns, nn.ModuleList)
+            assert isinstance(up_gnns, nn.ModuleList)
+            assert isinstance(up_same_gnns, nn.ModuleList)
+
             # Down
             mesh_rep_levels, mesh_same_rep, mesh_down_rep = self.mesh_down_step(
                 mesh_rep_levels,
diff --git a/neural_lam/models/step_predictors/graph/hi_lam_parallel.py b/neural_lam/models/step_predictors/graph/hi_lam_parallel.py
index fbab93ef..b70045c7 100644
--- a/neural_lam/models/step_predictors/graph/hi_lam_parallel.py
+++ b/neural_lam/models/step_predictors/graph/hi_lam_parallel.py
@@ -2,8 +2,6 @@
 Parallel hierarchical graph-based LAM model.
 """
 
-# Standard library
-
 # Third-party
 import torch
 import torch_geometric as pyg
@@ -40,7 +38,7 @@ def __init__(
         m2g_gnn_type: str = "InteractionNet",
         mesh_up_gnn_type: str = "InteractionNet",
         mesh_down_gnn_type: str = "InteractionNet",
-    ):
+    ) -> None:
         """
         Initialize the HiLAMParallel model.
 
@@ -119,8 +117,17 @@ def __init__(
             )
 
     def hi_processor_step(
-        self, mesh_rep_levels, mesh_same_rep, mesh_up_rep, mesh_down_rep
-    ):
+        self,
+        mesh_rep_levels: list[torch.Tensor],
+        mesh_same_rep: list[torch.Tensor],
+        mesh_up_rep: list[torch.Tensor],
+        mesh_down_rep: list[torch.Tensor],
+    ) -> tuple[
+        list[torch.Tensor],
+        list[torch.Tensor],
+        list[torch.Tensor],
+        list[torch.Tensor],
+    ]:
         """
         Run all processor steps in parallel across all edge types and
         hierarchy levels.
@@ -156,7 +163,7 @@ def hi_processor_step(
             mesh_rep_levels, dim=1
         )  # (B, num_mesh_nodes, hidden_dim)
         mesh_edge_rep = torch.cat(
-            mesh_same_rep + mesh_up_rep + mesh_down_rep, axis=1
+            mesh_same_rep + mesh_up_rep + mesh_down_rep, dim=1
         )  # (B, num_edges, hidden_dim)
 
         # Here, update mesh_*_rep and mesh_rep
@@ -170,13 +177,15 @@ def hi_processor_step(
             mesh_edge_rep, self.edge_split_sections, dim=1
         )
 
-        mesh_same_rep = mesh_edge_rep_sections[: self.num_levels]
-        mesh_up_rep = mesh_edge_rep_sections[
-            self.num_levels : self.num_levels + (self.num_levels - 1)
-        ]
-        mesh_down_rep = mesh_edge_rep_sections[
-            self.num_levels + (self.num_levels - 1) :
-        ]  # Last are down edges
+        mesh_same_rep = list(mesh_edge_rep_sections[: self.num_levels])
+        mesh_up_rep = list(
+            mesh_edge_rep_sections[
+                self.num_levels : self.num_levels + (self.num_levels - 1)
+            ]
+        )
+        mesh_down_rep = list(
+            mesh_edge_rep_sections[self.num_levels + (self.num_levels - 1) :]
+        )  # Last are down edges
 
         # TODO: We return all, even though only down edges really are used
         # later
diff --git a/neural_lam/models/step_predictors/graph/hierarchical.py b/neural_lam/models/step_predictors/graph/hierarchical.py
index 8baa99b5..c42e9dab 100644
--- a/neural_lam/models/step_predictors/graph/hierarchical.py
+++ b/neural_lam/models/step_predictors/graph/hierarchical.py
@@ -3,6 +3,7 @@
 # Standard library
 
 # Third-party
+import torch
 from torch import nn
 
 # Local
@@ -17,6 +18,15 @@ class BaseHiGraphModel(BaseGraphModel):
     Base class for hierarchical graph models.
     """
 
+    # Buffers or graph features that are lists of tensors in hierarchical models
+    mesh_static_features: list[torch.Tensor]
+    m2m_features: list[torch.Tensor]
+    mesh_up_features: list[torch.Tensor]
+    mesh_down_features: list[torch.Tensor]
+    m2m_edge_index: list[torch.Tensor]
+    mesh_up_edge_index: list[torch.Tensor]
+    mesh_down_edge_index: list[torch.Tensor]
+
     def __init__(
         self,
         datastore: BaseDatastore,
@@ -34,7 +44,7 @@ def __init__(
         m2g_gnn_type: str = "InteractionNet",
         mesh_up_gnn_type: str = "InteractionNet",
         mesh_down_gnn_type: str = "InteractionNet",
-    ):
+    ) -> None:
         """Extend :class:`BaseGraphModel` with hierarchical mesh structures."""
         super().__init__(
             datastore=datastore,
@@ -140,7 +150,7 @@ def __init__(
             ]
         )
 
-    def get_num_mesh(self):
+    def get_num_mesh(self) -> tuple[int, int]:
         """
         Compute mesh node counts used for encoding and decoding.
 
@@ -158,7 +168,7 @@ def get_num_mesh(self):
         )
         return num_mesh_nodes, num_mesh_nodes_ignore
 
-    def embedd_mesh_nodes(self):
+    def embedd_mesh_nodes(self) -> torch.Tensor:
         """
         Embed static mesh node features for the bottom level only;
         remaining levels are embedded at the start of ``process_step``.
@@ -173,7 +183,7 @@ def embedd_mesh_nodes(self):
         """
         return self.mesh_embedders[0](self.mesh_static_features[0])
 
-    def process_step(self, mesh_rep):
+    def process_step(self, mesh_rep: torch.Tensor) -> torch.Tensor:
         """
         Process the mesh representation across all hierarchy levels,
         implementing the full init-process-readout cycle.
@@ -282,8 +292,17 @@ def process_step(self, mesh_rep):
         return mesh_rep_levels[0]  # (B, num_mesh_nodes[0], hidden_dim)
 
     def hi_processor_step(
-        self, mesh_rep_levels, mesh_same_rep, mesh_up_rep, mesh_down_rep
-    ):
+        self,
+        mesh_rep_levels: list[torch.Tensor],
+        mesh_same_rep: list[torch.Tensor],
+        mesh_up_rep: list[torch.Tensor],
+        mesh_down_rep: list[torch.Tensor],
+    ) -> tuple[
+        list[torch.Tensor],
+        list[torch.Tensor],
+        list[torch.Tensor],
+        list[torch.Tensor],
+    ]:
         """
         Internal processor step between mesh init and read-out.
 
diff --git a/neural_lam/plot_graph.py b/neural_lam/plot_graph.py
index 77c9b3cf..09690ffe 100644
--- a/neural_lam/plot_graph.py
+++ b/neural_lam/plot_graph.py
@@ -3,7 +3,7 @@
 # Standard library
 import os
 from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
-from typing import Any, Optional
+from typing import Any
 
 # Third-party
 import numpy as np
@@ -24,7 +24,7 @@ def plot_graph(
     hierarchical: bool,
     graph_ldict: dict[str, Any],
     show_axis: bool = False,
-    save: Optional[str] = None,
+    save: str | None = None,
 ) -> go.Figure:
     """Build a 3D plotly figure of the graph structure.
 
diff --git a/neural_lam/train_model.py b/neural_lam/train_model.py
index f98065c4..dfa331cd 100644
--- a/neural_lam/train_model.py
+++ b/neural_lam/train_model.py
@@ -17,7 +17,8 @@
 
 # Local
 from . import utils
-from .config import load_config_and_datastore
+from .config import NeuralLAMConfig, load_config_and_datastore
+from .datastore.base import BaseDatastore
 from .gnn_layers import GNN_TYPES
 from .models import MODELS, ARForecaster, ForecasterModule
 from .weather_dataset import WeatherDataModule
@@ -26,7 +27,7 @@
 class AdaptiveHelpFormatter(ArgumentDefaultsHelpFormatter):
     """``--help`` formatter that scales the column width to the terminal."""
 
-    def __init__(self, prog):
+    def __init__(self, prog: str) -> None:
         """Pick a help-column width based on the current terminal size."""
         terminal_width = shutil.get_terminal_size(fallback=(100, 20)).columns
         width = max(80, min(terminal_width, 120))
@@ -38,7 +39,11 @@ def __init__(self, prog):
         )
 
 
-def load_forecaster_module_from_checkpoint(ckpt_path, config, datastore):
+def load_forecaster_module_from_checkpoint(
+    ckpt_path: str,
+    config: NeuralLAMConfig,
+    datastore: BaseDatastore,
+) -> ForecasterModule:
     """
     Reconstruct a ForecasterModule from a checkpoint without requiring the
     caller to know the original architecture kwargs.
@@ -73,7 +78,7 @@ def load_forecaster_module_from_checkpoint(ckpt_path, config, datastore):
 
 
 @logger.catch
-def main(input_args=None):
+def main(input_args: list[str] | None = None) -> None:
     """Main function for training and evaluating models."""
     parser = ArgumentParser(
         description="Train or evaluate MLWP models for LAM",
@@ -429,6 +434,7 @@ def main(input_args=None):
         device_name = "cpu"
 
     # Set devices to use
+    devices: str | list[int]
     if args.devices == ["auto"]:
         devices = "auto"
     else:
diff --git a/neural_lam/utils.py b/neural_lam/utils.py
index 942eb206..7f75fa62 100644
--- a/neural_lam/utils.py
+++ b/neural_lam/utils.py
@@ -1,6 +1,7 @@
 """Utility helpers shared across Neural-LAM training and evaluation."""
 
 # Standard library
+import argparse
 import datetime
 import os
 import shutil
@@ -9,7 +10,10 @@
 import warnings
 from functools import cache
 from pathlib import Path
-from typing import Any, Iterator, Union, overload
+from typing import TYPE_CHECKING, Any, Iterator, overload
+
+if TYPE_CHECKING:
+    from .datastore.base import BaseDatastore
 
 # Third-party
 import pytorch_lightning as pl
@@ -60,8 +64,8 @@ def __getitem__(self, key: slice) -> list[torch.Tensor]:
         """Slice-indexed access overload; see the implementation below."""
 
     def __getitem__(
-        self, key: Union[int, slice]
-    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        self, key: int | slice
+    ) -> torch.Tensor | list[torch.Tensor]:
         """Return the buffer(s) at ``key``.
 
         Supports integer indexing (with Python-style negative indices)
@@ -92,13 +96,13 @@ def __iter__(self) -> Iterator[torch.Tensor]:
         """Iterate over the registered buffers in ascending index order."""
         return (self[i] for i in range(len(self)))
 
-    def __itruediv__(self, other: float) -> "BufferList":
+    def __itruediv__(self, other: float | torch.Tensor) -> "BufferList":
         """
         Divide each element in list with other.
 
         Parameters
         ----------
-        other : float
+        other : float or torch.Tensor
             The value to divide by.
 
         Returns
@@ -108,13 +112,13 @@ def __itruediv__(self, other: float) -> "BufferList":
         """
         return self.__imul__(1.0 / other)
 
-    def __imul__(self, other: float) -> "BufferList":
+    def __imul__(self, other: float | torch.Tensor) -> "BufferList":
         """
         Multiply each element in list with other.
 
         Parameters
         ----------
-        other : float
+        other : float or torch.Tensor
             The value to multiply by.
 
         Returns
@@ -254,7 +258,7 @@ def zero_index_g2m(
 
 
 def load_graph(
-    graph_dir_path: Union[str, Path], device: str = "cpu"
+    graph_dir_path: str | Path, device: str = "cpu"
 ) -> tuple[bool, dict[str, Any]]:
     """Load all tensors representing the graph from `graph_dir_path`.
 
@@ -377,6 +381,9 @@ def loads_file(fn: str) -> Any:
         len(mesh_static_features) == n_levels
     ), "Inconsistent number of levels in mesh"
 
+    m2m_edge_index_out: Any
+    m2m_features_out: Any
+    mesh_static_features_out: Any
     if hierarchical:
         # Load up and down edges and features
         mesh_up_edge_index = BufferList(
@@ -407,14 +414,16 @@ def loads_file(fn: str) -> Any:
         mesh_down_features = BufferList(mesh_down_features, persistent=False)
         mesh_down_features /= longest_edge
 
-        mesh_static_features = BufferList(
+        mesh_static_features_out = BufferList(
             mesh_static_features, persistent=False
         )
+        m2m_edge_index_out = m2m_edge_index
+        m2m_features_out = m2m_features
     else:
         # Extract single mesh level
-        m2m_edge_index = m2m_edge_index[0]
-        m2m_features = m2m_features[0]
-        mesh_static_features = mesh_static_features[0]
+        m2m_edge_index_out = m2m_edge_index[0]
+        m2m_features_out = m2m_features[0]
+        mesh_static_features_out = mesh_static_features[0]
 
         mesh_up_edge_index = BufferList([], persistent=False)
         mesh_down_edge_index = BufferList([], persistent=False)
@@ -424,15 +433,15 @@ def loads_file(fn: str) -> Any:
     return hierarchical, {
         "g2m_edge_index": g2m_edge_index,
         "m2g_edge_index": m2g_edge_index,
-        "m2m_edge_index": m2m_edge_index,
+        "m2m_edge_index": m2m_edge_index_out,
         "mesh_up_edge_index": mesh_up_edge_index,
         "mesh_down_edge_index": mesh_down_edge_index,
         "g2m_features": g2m_features,
         "m2g_features": m2g_features,
-        "m2m_features": m2m_features,
+        "m2m_features": m2m_features_out,
         "mesh_up_features": mesh_up_features,
         "mesh_down_features": mesh_down_features,
-        "mesh_static_features": mesh_static_features,
+        "mesh_static_features": mesh_static_features_out,
     }
 
 
@@ -458,7 +467,7 @@ def make_mlp(blueprint: list[int], layer_norm: bool = True) -> nn.Sequential:
     hidden_layers = len(blueprint) - 2
     assert hidden_layers >= 0, "Invalid MLP blueprint"
 
-    layers = []
+    layers: list[nn.Module] = []
     for layer_i, (dim1, dim2) in enumerate(zip(blueprint[:-1], blueprint[1:])):
         layers.append(nn.Linear(dim1, dim2))
         if layer_i != hidden_layers:
@@ -616,8 +625,11 @@ def init_training_logger_metrics(
 
 @rank_zero_only
 def setup_training_logger(
-    datastore: Any, args: Any, run_name: str, run_dir: str
-) -> Any:
+    datastore: "BaseDatastore",
+    args: argparse.Namespace,
+    run_name: str,
+    run_dir: str,
+) -> pl.loggers.Logger:
     """
     Set up the training logger (WandB or MLFlow).
 
@@ -666,7 +678,7 @@ def setup_training_logger(
         return pl.loggers.WandbLogger(
             project=args.logger_project,
             name=None if args.wandb_id else run_name,
-            config=dict(training=vars(args), datastore=datastore._config),
+            config=dict(training=vars(args), datastore=datastore.config),
             resume=wandb_resume,
             id=args.wandb_id,
             save_dir=run_dir,
@@ -689,7 +701,7 @@ def setup_training_logger(
             save_dir=run_dir,
         )
         training_logger.log_hyperparams(
-            dict(training=vars(args), datastore=datastore._config)
+            dict(training=vars(args), datastore=datastore.config)
         )
         return training_logger
     else:
diff --git a/neural_lam/vis.py b/neural_lam/vis.py
index 1e3dd415..326dfce3 100644
--- a/neural_lam/vis.py
+++ b/neural_lam/vis.py
@@ -2,7 +2,7 @@
 
 # Standard library
 import warnings
-from typing import Optional
+from typing import Any
 
 # Third-party
 import cartopy.crs as ccrs
@@ -16,6 +16,7 @@
 import numpy as np
 import torch
 import xarray as xr
+from cartopy.mpl.geoaxes import GeoAxes
 
 # Local
 from . import utils
@@ -122,7 +123,7 @@ def _get_heatmap_var_labels(datastore: BaseRegularGridDatastore) -> list[str]:
     ]
 
 
-def _to_heatmap_matrix(values) -> np.ndarray:
+def _to_heatmap_matrix(values: Any) -> np.ndarray:
     """
     Convert heatmap inputs to a ``(num_state_vars, pred_steps)`` matrix.
 
@@ -228,7 +229,9 @@ def _get_heatmap_color_values(
         If ``normalization`` is not one of ``'state_std'`` or ``'diff_std'``.
     """
 
-    def _per_var_fallback():
+    def _per_var_fallback() -> (
+        tuple[np.ndarray, str, matplotlib.colors.Colormap]
+    ):
         """
         Normalize errors by per-variable maximum value.
 
@@ -340,7 +343,7 @@ def _get_annotation_text_color(
 
 
 def plot_on_axis(
-    ax: matplotlib.axes.Axes,
+    ax: GeoAxes,
     da: xr.DataArray,
     datastore: BaseRegularGridDatastore,
     vmin: float | None = None,
@@ -469,7 +472,7 @@ def plot_on_axis(
 def plot_error_heatmap(
     errors: torch.Tensor,
     datastore: BaseRegularGridDatastore,
-    title: Optional[str] = None,
+    title: str | None = None,
     normalization: str = "state_std",
 ) -> matplotlib.figure.Figure:
     """
@@ -546,7 +549,9 @@ def plot_error_heatmap(
                 )
             else:
                 formatted_error = str(error)
-            text_color = _get_annotation_text_color(color_values_np[j, i], im)
+            text_color = _get_annotation_text_color(
+                float(color_values_np[j, i]), im
+            )
             ax.text(
                 i,
                 j,
@@ -586,7 +591,7 @@ def plot_error_heatmap(
 def plot_error_map(
     errors: torch.Tensor,
     datastore: BaseRegularGridDatastore,
-    title: Optional[str] = None,
+    title: str | None = None,
 ) -> matplotlib.figure.Figure:
     """
     Deprecated: use :func:`plot_error_heatmap` instead.
@@ -618,8 +623,8 @@ def plot_prediction(
     datastore: BaseRegularGridDatastore,
     da_prediction: xr.DataArray,
     da_target: xr.DataArray,
-    title: Optional[str] = None,
-    vrange: Optional[tuple[float, float]] = None,
+    title: str | None = None,
+    vrange: tuple[float, float] | None = None,
     boundary_alpha: float = 0.7,
     crop_to_interior: bool = True,
     colorbar_label: str = "",
@@ -702,8 +707,8 @@ def plot_prediction(
 def plot_spatial_error(
     error: torch.Tensor,
     datastore: BaseRegularGridDatastore,
-    title: Optional[str] = None,
-    vrange: Optional[tuple[float, float]] = None,
+    title: str | None = None,
+    vrange: tuple[float, float] | None = None,
     boundary_alpha: float = 0.7,
     crop_to_interior: bool = True,
     colorbar_label: str = "",
@@ -767,7 +772,7 @@ def plot_spatial_error(
         pad=0.02,
     )
     cbar.ax.tick_params(labelsize=_TICK_SIZE)
-    cbar.formatter.set_powerlimits((-3, 3))
+    cbar.formatter.set_powerlimits((-3, 3))  # type: ignore[attr-defined]
     if colorbar_label:
         cbar.set_label(_tex_safe(colorbar_label), size=_LABEL_SIZE)
 
diff --git a/neural_lam/weather_dataset.py b/neural_lam/weather_dataset.py
index 4168396a..93c13641 100644
--- a/neural_lam/weather_dataset.py
+++ b/neural_lam/weather_dataset.py
@@ -3,7 +3,7 @@
 # Standard library
 import datetime
 import warnings
-from typing import Iterator, Optional, Union
+from typing import Iterator
 
 # Third-party
 import numpy as np
@@ -70,16 +70,17 @@ def __init__(
         self.num_future_forcing_steps = num_future_forcing_steps
         self.load_single_member = load_single_member
 
-        self.da_state = self.datastore.get_dataarray(
+        da_state = self.datastore.get_dataarray(
             category="state", split=self.split
         )
-        self.da_forcing = self.datastore.get_dataarray(
-            category="forcing", split=self.split
-        )
-        if self.da_state is None:
+        if da_state is None:
             raise ValueError(
                 "The datastore must provide state data for the WeatherDataset."
             )
+        self.da_state = da_state
+        self.da_forcing = self.datastore.get_dataarray(
+            category="forcing", split=self.split
+        )
 
         if self.datastore.is_ensemble and self.load_single_member:
             warnings.warn(
@@ -465,7 +466,7 @@ def _build_item_dataarrays(
 
     def __getitem__(
         self, idx: int
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, np.ndarray]:
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         Return a single training sample, which consists of the initial states,
         target states, forcing and batch times.
@@ -533,7 +534,9 @@ def __getitem__(
 
     def __iter__(
         self,
-    ) -> Iterator[tuple[torch.Tensor, torch.Tensor, torch.Tensor, np.ndarray]]:
+    ) -> Iterator[
+        tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
+    ]:
         """
         Convenience method to iterate over the dataset.
 
@@ -547,9 +550,9 @@ def __iter__(
     def create_dataarray_from_tensor(
         self,
         tensor: torch.Tensor,
-        time: Union[datetime.datetime, list[datetime.datetime]],
+        time: datetime.datetime | list[datetime.datetime] | np.ndarray,
         category: str,
-    ):
+    ) -> xr.DataArray:
         """
         Construct a xarray.DataArray from a `pytorch.Tensor` with coordinates
         for `grid_index`, `time` and `{category}_feature` matching the shape
@@ -580,7 +583,7 @@ def create_dataarray_from_tensor(
             The constructed DataArray.
         """
 
-        def _is_listlike(obj):
+        def _is_listlike(obj: object) -> bool:
             """Return ``True`` for list/tuple/ndarray-like containers."""
             return hasattr(obj, "__iter__") and not isinstance(obj, str)
 
@@ -686,17 +689,17 @@ def __init__(
         self.load_single_member = load_single_member
         self.batch_size = batch_size
         self.num_workers: int = num_workers
-        self.train_dataset: Optional[WeatherDataset] = None
-        self.val_dataset: Optional[WeatherDataset] = None
-        self.test_dataset: Optional[WeatherDataset] = None
-        self.multiprocessing_context: Union[str, None] = None
+        self.train_dataset: WeatherDataset | None = None
+        self.val_dataset: WeatherDataset | None = None
+        self.test_dataset: WeatherDataset | None = None
+        self.multiprocessing_context: str | None = None
         self.eval_split = eval_split
         if num_workers > 0:
             # default to spawn for now, as the default on linux "fork" hangs
             # when using dask (which the npyfilesmeps datastore uses)
             self.multiprocessing_context = "spawn"
 
-    def setup(self, stage: Optional[str] = None) -> None:
+    def setup(self, stage: str | None = None) -> None:
         """
         Instantiate datasets for the requested trainer stage.
 
@@ -737,6 +740,7 @@ def setup(self, stage: Optional[str] = None) -> None:
 
     def train_dataloader(self) -> torch.utils.data.DataLoader:
         """Load train dataset."""
+        assert self.train_dataset is not None
         return torch.utils.data.DataLoader(
             self.train_dataset,
             batch_size=self.batch_size,
@@ -749,6 +753,7 @@ def train_dataloader(self) -> torch.utils.data.DataLoader:
 
     def val_dataloader(self) -> torch.utils.data.DataLoader:
         """Load validation dataset."""
+        assert self.val_dataset is not None
         return torch.utils.data.DataLoader(
             self.val_dataset,
             batch_size=self.batch_size,
@@ -761,6 +766,7 @@ def val_dataloader(self) -> torch.utils.data.DataLoader:
 
     def test_dataloader(self) -> torch.utils.data.DataLoader:
         """Load test dataset."""
+        assert self.test_dataset is not None
         return torch.utils.data.DataLoader(
             self.test_dataset,
             batch_size=self.batch_size,
diff --git a/pyproject.toml b/pyproject.toml
index 113329e1..d748cf1a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -51,7 +51,15 @@ gpu = ["torch>=2.12,<2.13"]        # CUDA 13.0, default GPU build
 gpu-cu128 = ["torch>=2.11,<2.12"]  # CUDA 12.8, last torch series with cu128 wheels
 
 [dependency-groups]
-dev = ["pre-commit>=3.8.0", "pytest>=8.3.2", "pooch>=1.8.2"]
+dev = [
+    "pre-commit>=3.8.0",
+    "pytest>=8.3.2",
+    "pooch>=1.8.2",
+    "mypy>=2.1.0",
+    "types-pyyaml",
+    "types-pillow",
+    "types-tqdm",
+]
 
 [tool.uv]
 # uv-specific configuration for PyTorch CPU/GPU variant selection.
@@ -121,6 +129,13 @@ per-file-ignores = [
 [tool.codespell]
 skip = "requirements/*"
 
+[tool.mypy]
+python_version = "3.10"
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+ignore_missing_imports = true
+exclude = ["^tests/", "^docs/", "^build/"]
+
 # Pylint config
 [tool.pylint]
 ignore = [
diff --git a/uv.lock b/uv.lock
index b9d5a7ed..4115acd3 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,27 +2,32 @@ version = 1
 revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version < '3.11' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
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 version = "12.9.6"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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 version = "13.2.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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 version = "12.8.1"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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 source = { registry = "https://pypi.org/simple" }
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 version = "2.18.1"
@@ -2554,23 +2761,28 @@ name = "networkx"
 version = "3.6.1"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
@@ -2605,9 +2817,9 @@ dependencies = [
     { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
     { name = "shapely" },
     { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
-    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu') or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
+    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.15' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu') or (python_full_version >= '3.15' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (python_full_version >= '3.15' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
     { name = "torch", version = "2.12.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128')" },
-    { name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu') or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
+    { name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.15' and extra == 'extra-10-neural-lam-cpu') or (python_full_version < '3.15' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (python_full_version < '3.15' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (python_full_version < '3.15' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
     { name = "torch", version = "2.12.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "extra == 'extra-10-neural-lam-gpu' or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
     { name = "torch-geometric" },
     { name = "tueplots" },
@@ -2616,8 +2828,8 @@ dependencies = [
 
 [package.optional-dependencies]
 cpu = [
-    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu') or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
-    { name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu') or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
+    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.15' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu') or (python_full_version >= '3.15' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (python_full_version >= '3.15' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
+    { name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.15' and extra == 'extra-10-neural-lam-cpu') or (python_full_version < '3.15' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (python_full_version < '3.15' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (python_full_version < '3.15' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
 ]
 gpu = [
     { name = "torch", version = "2.12.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" } },
@@ -2628,9 +2840,13 @@ gpu-cu128 = [
 
 [package.dev-dependencies]
 dev = [
+    { name = "mypy" },
     { name = "pooch" },
     { name = "pre-commit" },
     { name = "pytest" },
+    { name = "types-pillow" },
+    { name = "types-pyyaml" },
+    { name = "types-tqdm" },
 ]
 
 [package.metadata]
@@ -2663,9 +2879,13 @@ provides-extras = ["cpu", "gpu", "gpu-cu128"]
 
 [package.metadata.requires-dev]
 dev = [
+    { name = "mypy", specifier = ">=2.1.0" },
     { name = "pooch", specifier = ">=1.8.2" },
     { name = "pre-commit", specifier = ">=3.8.0" },
     { name = "pytest", specifier = ">=8.3.2" },
+    { name = "types-pillow" },
+    { name = "types-pyyaml" },
+    { name = "types-tqdm" },
 ]
 
 [[package]]
@@ -2716,23 +2936,28 @@ name = "numcodecs"
 version = "0.16.5"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
@@ -2839,23 +3064,28 @@ name = "numpy"
 version = "2.4.4"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
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+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
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 ]
 
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+version = "1.1.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5a/82/42f767fc1c1143d6fd36efb827202a2d997a375e160a71eb2888a925aac1/pathspec-1.1.1.tar.gz", hash = "sha256:17db5ecd524104a120e173814c90367a96a98d07c45b2e10c2f3919fff91bf5a", size = 135180, upload-time = "2026-04-27T01:46:08.907Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f1/d9/7fb5aa316bc299258e68c73ba3bddbc499654a07f151cba08f6153988714/pathspec-1.1.1-py3-none-any.whl", hash = "sha256:a00ce642f577bf7f473932318056212bc4f8bfdf53128c78bbd5af0b9b20b189", size = 57328, upload-time = "2026-04-27T01:46:07.06Z" },
+]
+
 [[package]]
 name = "pillow"
 version = "12.1.1"
@@ -4083,23 +4322,28 @@ name = "pyproj"
 version = "3.7.2"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
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@@ -4236,9 +4480,9 @@ dependencies = [
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+    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.15' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu') or (python_full_version >= '3.15' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (python_full_version >= '3.15' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (sys_platform != 'darwin' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128') or (extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128')" },
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     { name = "torchmetrics" },
     { name = "tqdm" },
@@ -4453,23 +4697,28 @@ name = "scikit-learn"
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@@ -4588,23 +4837,28 @@ name = "scipy"
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+    "python_full_version >= '3.15' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
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     "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
@@ -4854,23 +5108,28 @@ name = "spherical-geometry"
 version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra == 'extra-10-neural-lam-gpu-cu128'",
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-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra == 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
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+    "python_full_version >= '3.15' and sys_platform != 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
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+    "python_full_version >= '3.15' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
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     "python_full_version == '3.12.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
-    "python_full_version >= '3.14' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version >= '3.15' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
+    "python_full_version == '3.14.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.13.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.12.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
     "python_full_version == '3.11.*' and extra != 'extra-10-neural-lam-cpu' and extra != 'extra-10-neural-lam-gpu' and extra != 'extra-10-neural-lam-gpu-cu128'",
@@ -5083,7 +5342,8 @@ name = "torch"
 version = "2.11.0+cu128"
 source = { registry = "https://download.pytorch.org/whl/cu128" }
 resolution-markers = [
-    "python_full_version >= '3.14'",
+    "python_full_version >= '3.15'",
+    "python_full_version == '3.14.*'",
     "python_full_version == '3.13.*'",
     "python_full_version == '3.12.*'",
     "python_full_version == '3.11.*'",
@@ -5135,21 +5395,21 @@ name = "torch"
 version = "2.12.0"
 source = { registry = "https://download.pytorch.org/whl/cpu" }
 resolution-markers = [
-    "python_full_version >= '3.14' and sys_platform == 'darwin'",
+    "python_full_version == '3.14.*' and sys_platform == 'darwin'",
     "python_full_version == '3.13.*' and sys_platform == 'darwin'",
     "python_full_version == '3.12.*' and sys_platform == 'darwin'",
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
     "python_full_version < '3.11' and sys_platform == 'darwin'",
 ]
 dependencies = [
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 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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     "python_full_version == '3.11.*' and sys_platform != 'darwin'",
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 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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