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       - pypi: ./
       osx-arm64:
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@@ -4168,7 +4206,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/x265-3.5-hbc6ce65_3.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.9.0-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2026.2.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.11-hd74edd7_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxdmcp-1.1.5-hd74edd7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2024.9.0-pyhd8ed1ab_0.conda
@@ -4181,6 +4219,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py311ha60cc69_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
+      - pypi: https://files.pythonhosted.org/packages/0c/d5/c5db1ea3394c6e1732fb3286b3bd878b59507a8f77d32a2cebda7d7b7cd4/donfig-0.8.1.post1-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/5d/ef/21ccfaab3d5078d41efe8612e0ed0bfc9ce22475de074162a91a25f7980d/google_crc32c-1.8.0-cp311-cp311-macosx_12_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/8d/9e/f7caf7486a22c3f8dde60228a9905c73dd676cdcacbdaa4390acfc9ae959/h5pyd-0.18.0.tar.gz
       - pypi: https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl
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@@ -4188,6 +4228,7 @@ environments:
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+      - pypi: https://files.pythonhosted.org/packages/0e/cc/0d97ef55dda48cb0f93d7b92d761208e7a99bd2eea6b0e859426e6a99a21/numcodecs-0.16.5-cp311-cp311-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/62/5e/3a6a3e90f35cea3853c45e5d5fb9b7192ce4384616f932cf7591298ab6e1/numpydoc-1.10.0-py3-none-any.whl
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@@ -4197,6 +4238,7 @@ environments:
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       - pypi: https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/44/15/bb13b4913ef95ad5448490821eee4671d0e67673342e4d4070854e5fe081/zarr-3.1.5-py3-none-any.whl
       - pypi: ./
 packages:
 - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
@@ -6440,13 +6482,13 @@ packages:
   purls: []
   size: 81202
   timestamp: 1755102333712
-- pypi: https://files.pythonhosted.org/packages/e5/61/8ac32517c1e856677282c34f2e7812d6c328fa02b8f4067ab80e77fdc9c9/grpcio-1.78.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/13/3c/ac769c8ded1bcb26bb119fb472d3374b481b3cf059a0875db9fc77139c17/grpcio-1.78.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
   name: grpcio
-  version: 1.78.0
-  sha256: 85f93781028ec63f383f6bc90db785a016319c561cc11151fbb7b34e0d012303
+  version: 1.78.1
+  sha256: a6afd191551fd72e632367dfb083e33cd185bf9ead565f2476bba8ab864ae496
   requires_dist:
   - typing-extensions~=4.12
-  - grpcio-tools>=1.78.0 ; extra == 'protobuf'
+  - grpcio-tools>=1.78.1 ; extra == 'protobuf'
   requires_python: '>=3.9'
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/grpcio-1.59.3-py311hf5d242d_0.conda
   sha256: d881b0a9a45e09e2c3dacf68bfb403088d165a5cac7de8e775de5d205bf09718
@@ -10539,7 +10581,8 @@ packages:
   - pytest>=5.2
   - scipy>=1.0.0
   - sphinx>=7.0
-  - xarray>=2023.0
+  - xarray>=2024.0
+  - zarr>=2.0.0,<4
   - pre-commit ; extra == 'dev'
   - pylint ; extra == 'dev'
   - ruff>=0.4 ; extra == 'dev'
@@ -11771,6 +11814,23 @@ packages:
   - pkg:pypi/python-dateutil?source=hash-mapping
   size: 222742
   timestamp: 1709299922152
+- pypi: https://files.pythonhosted.org/packages/c0/5c/e2b18e66d73b69de87c198cba8744934b91247d8ab657a7253b591f4cf23/python_discovery-1.0.0-py3-none-any.whl
+  name: python-discovery
+  version: 1.0.0
+  sha256: 7cd9eaf3b1845875e22084f92d0ec2e309be2a3f839a9eb52980d647b72bd891
+  requires_dist:
+  - filelock>=3.15.4
+  - platformdirs>=4.3.6,<5
+  - furo>=2025.12.19 ; extra == 'docs'
+  - sphinx-autodoc-typehints>=3.6.3 ; extra == 'docs'
+  - sphinx>=9.1 ; extra == 'docs'
+  - sphinxcontrib-mermaid>=2 ; extra == 'docs'
+  - covdefaults>=2.3 ; extra == 'testing'
+  - coverage>=7.5.4 ; extra == 'testing'
+  - pytest-mock>=3.14 ; extra == 'testing'
+  - pytest>=8.3.5 ; extra == 'testing'
+  - setuptools>=75.1 ; extra == 'testing'
+  requires_python: '>=3.8'
 - conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.20.0-pyhd8ed1ab_0.conda
   sha256: 7d8c931b89c9980434986b4deb22c2917b58d9936c3974139b9c10ae86fdfe60
   md5: b98d2018c01ce9980c03ee2850690fab
@@ -13557,10 +13617,10 @@ packages:
   - pkg:pypi/websocket-client?source=hash-mapping
   size: 47066
   timestamp: 1713923494501
-- pypi: https://files.pythonhosted.org/packages/ad/e4/8d97cca767bcc1be76d16fb76951608305561c6e056811587f36cb1316a8/werkzeug-3.1.5-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/4d/ec/d58832f89ede95652fd01f4f24236af7d32b70cab2196dfcc2d2fd13c5c2/werkzeug-3.1.6-py3-none-any.whl
   name: werkzeug
-  version: 3.1.5
-  sha256: 5111e36e91086ece91f93268bb39b4a35c1e6f1feac762c9c822ded0a4e322dc
+  version: 3.1.6
+  sha256: 7ddf3357bb9564e407607f988f683d72038551200c704012bb9a4c523d42f131
   requires_dist:
   - markupsafe>=2.1.1
   - watchdog>=2.3 ; extra == 'watchdog'
@@ -13654,41 +13714,44 @@ packages:
   purls: []
   size: 1832744
   timestamp: 1646609481185
-- conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.9.0-pyhd8ed1ab_1.conda
-  sha256: 8bb5b522cdf1905d831a9b371a3a3bd2932a9f53398332fbd38ed3442015bbaf
-  md5: dc790d427d89b85ae12fc094e264833f
+- conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2026.2.0-pyhcf101f3_0.conda
+  sha256: 1d49f2c80c63913c5a9a525b64434a30cf1386502d0f24607db61bd46fa36a40
+  md5: b1b3a2477c1b888f15bbef01d7a9615f
   depends:
-  - numpy >=1.24
-  - packaging >=23.1
-  - pandas >=2.1
-  - python >=3.10
+  - python >=3.11
+  - numpy >=1.26
+  - packaging >=24.1
+  - pandas >=2.2
+  - python
   constrains:
-  - seaborn-base >=0.12
-  - distributed >=2023.9
-  - scipy >=1.11
+  - bottleneck >=1.4
+  - cartopy >=0.23
+  - cftime >=1.6
+  - dask-core >=2024.6
+  - distributed >=2024.6
+  - flox >=0.9
+  - h5netcdf >=1.3
+  - h5py >=3.11
+  - hdf5 >=1.14
+  - iris >=3.9
+  - matplotlib-base >=3.8
+  - nc-time-axis >=1.4
   - netcdf4 >=1.6.0
+  - numba >=0.60
+  - numbagg >=0.8
+  - pint >=0.24
+  - pydap >=3.5.0
+  - scipy >=1.13
+  - seaborn-base >=0.13
+  - sparse >=0.15
   - toolz >=0.12
-  - nc-time-axis >=1.4
-  - cftime >=1.6
-  - h5netcdf >=1.2
-  - matplotlib-base >=3.7
-  - h5py >=3.8
-  - zarr >=2.16
-  - hdf5 >=1.12
-  - numba >=0.57
-  - iris >=3.7
-  - cartopy >=0.22
-  - dask-core >=2023.9
-  - flox >=0.7
-  - bottleneck >=1.3
-  - pint >=0.22
-  - sparse >=0.14
+  - zarr >=2.18
   license: Apache-2.0
   license_family: APACHE
   purls:
-  - pkg:pypi/xarray?source=hash-mapping
-  size: 801066
-  timestamp: 1728453306227
+  - pkg:pypi/xarray?source=compressed-mapping
+  size: 1011911
+  timestamp: 1771083999178
 - conda: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.1-hb711507_2.conda
   sha256: 416aa55d946ce4ab173ab338796564893a2f820e80e04e098ff00c25fb981263
   md5: 8637c3e5821654d0edf97e2b0404b443
diff --git a/pyproject.toml b/pyproject.toml
index 79c16ba98..61d23aaee 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,7 +41,8 @@ dependencies = [
   "pytest>=5.2",
   "scipy>=1.0.0",
   "sphinx>=7.0",
-  "xarray>=2023.0"
+  "xarray>=2024.0",
+  "zarr>=2.0.0,<4",
 ]
 
 # If used, cause glibc conflict
@@ -298,7 +299,7 @@ matplotlib = ">=3.1"
 numpy = "~=1.7"
 pandas = ">=2.0"
 scipy = ">=1.0.0"
-xarray = ">=2023.0"
+xarray = ">=2024.0"
 
 [tool.pixi.pypi-dependencies]
 NREL-sup3r = { path = ".", editable = true }
diff --git a/sup3r/models/__init__.py b/sup3r/models/__init__.py
index 0cb5f1bf5..185a921db 100644
--- a/sup3r/models/__init__.py
+++ b/sup3r/models/__init__.py
@@ -7,6 +7,7 @@
 from .multi_step import MultiStepGan, MultiStepSurfaceMetGan, SolarMultiStepGan
 from .solar_cc import SolarCC
 from .surface import SurfaceSpatialMetModel
+from .training_config import TrainingConfig
 from .with_obs import Sup3rGanWithObs
 
 SPATIAL_FIRST_MODELS = (MultiStepSurfaceMetGan, SolarMultiStepGan)
diff --git a/sup3r/models/abstract.py b/sup3r/models/abstract.py
index eb819ec9a..e7ad79356 100644
--- a/sup3r/models/abstract.py
+++ b/sup3r/models/abstract.py
@@ -51,6 +51,10 @@ def __init__(self):
         self._stdevs = None
         self._train_record = pd.DataFrame()
         self._val_record = pd.DataFrame()
+        self._swa_weights = None
+        self._swa_n = 0
+        self._swa_enabled = False
+        self._pre_swa_weights = None
 
     def load_network(self, model, name):
         """Load a CustomNetwork object from hidden layers config, .json file
@@ -722,6 +726,147 @@ def early_stop(history, column, threshold=0.005, n_epoch=5):
 
         return stop
 
+    def enable_swa(self):
+        """Enable stochastic weight averaging."""
+        self._swa_enabled = True
+        self._swa_weights = None
+        self._swa_n = 0
+        logger.info('SWA enabled')
+
+    def update_swa(self):
+        """Update the SWA running average with current model weights.
+
+        This should be called at the end of epochs where you want to include
+        the current weights in the average (e.g., after each epoch in the
+        last 25% of training, or at the end of each LR cycle).
+        """
+        if not self._swa_enabled:
+            return
+
+        current_weights = [w.numpy() for w in self.weights]
+
+        if self._swa_weights is None:
+            # First snapshot
+            self._swa_weights = current_weights
+            self._swa_n = 1
+        else:
+            for i, w in enumerate(current_weights):
+                self._swa_weights[i] = (
+                    self._swa_weights[i] * self._swa_n + w
+                ) / (self._swa_n + 1)
+            self._swa_n += 1
+
+        logger.info(f'Updated SWA weights (n={self._swa_n})')
+
+    def apply_swa(self, config, epoch, extras):
+        """Apply SWA updates if enabled in config.
+
+        Parameters
+        ----------
+        config : TrainingConfig
+            Training configuration object.
+        epoch : int
+            Current epoch number.
+        extras : dict
+            Dictionary of extra information to log for the current epoch.
+            This is updated in-place with any SWA-related information
+            (e.g., swa_n) and returned at the end.
+
+        Returns
+        -------
+        extras : dict
+            Updated dictionary of extra information to log for the
+            current epoch.
+        """
+        if config.swa_start is not None and epoch >= config.swa_start:
+            # Switch to constant LR if specified (only once)
+            if config.swa_lr is not None and epoch == config.swa_start:
+                self.update_optimizer('all', learning_rate=config.swa_lr)
+                logger.info(f'Switched to SWA constant LR: {config.swa_lr}')
+
+            # Update SWA weights at specified frequency
+            if (epoch - config.swa_start) % config.swa_freq == 0:
+                self.update_swa()
+                extras['swa_n'] = self._swa_n
+
+        return extras
+
+    def swap_swa_weights(self):
+        """Replace current model weights with SWA averaged weights.
+
+        Call this after training is complete to use the averaged weights.
+        """
+        if self._swa_weights is None:
+            logger.warning('No SWA weights to swap')
+            return
+
+        logger.info(
+            f'Swapping to SWA weights (averaged over {self._swa_n} snapshots)'
+        )
+
+        # Store original weights as backup
+        self._pre_swa_weights = [w.numpy() for w in self.weights]
+
+        # Set model weights to SWA averages
+        for weight_var, swa_weight in zip(self.weights, self._swa_weights):
+            weight_var.assign(swa_weight)
+
+    def restore_pre_swa_weights(self):
+        """Restore weights from before SWA swap (for comparison/debugging)."""
+        if self._pre_swa_weights is None:
+            logger.warning('No pre-SWA weights to restore')
+            return
+
+        for weight_var, pre_swa_weight in zip(
+            self.weights, self._pre_swa_weights
+        ):
+            weight_var.assign(pre_swa_weight)
+        logger.info('Restored pre-SWA weights')
+
+    def update_bn_stats(self, batch_handler, n_batches=None):
+        """Update batch normalization statistics after swapping to SWA weights.
+
+        This is critical because BN layers have running statistics computed
+        during training with the original weights, not the SWA averaged
+        weights.
+
+        Parameters
+        ----------
+        batch_handler : sup3r.preprocessing.BatchHandler
+            BatchHandler to iterate through for BN updates
+        n_batches : int | None
+            Number of batches to use. If None, uses all available batches.
+        """
+        has_bn_layers = any(
+            isinstance(layer, (tf.keras.layers.BatchNormalization,))
+            for layer in self.generator.layers
+        )
+        if not has_bn_layers:
+            logger.info(
+                'No batch normalization layers found, skipping BN stats update'
+            )
+            return
+
+        logger.info('Updating batch normalization statistics for SWA model...')
+
+        # Reset BN layer statistics
+        for layer in self.generator.layers:
+            if isinstance(layer, (tf.keras.layers.BatchNormalization,)):
+                layer.moving_mean.assign(tf.zeros_like(layer.moving_mean))
+                layer.moving_variance.assign(
+                    tf.ones_like(layer.moving_variance)
+                )
+
+        # Do forward passes to recompute statistics
+        count = 0
+        for batch in batch_handler:
+            if n_batches is not None and count >= n_batches:
+                break
+            _ = self.generate(batch.low_res, norm_in=True)
+            count += 1
+
+        logger.info(f'Updated BN stats using {count} batches')
+
     @abstractmethod
     def save(self, out_dir):
         """Save the model with its sub-networks to a directory.
diff --git a/sup3r/models/base.py b/sup3r/models/base.py
index 0fa852d28..1bb5afc46 100644
--- a/sup3r/models/base.py
+++ b/sup3r/models/base.py
@@ -15,6 +15,7 @@
 
 from .abstract import AbstractSingleModel
 from .interface import AbstractInterface
+from .training_config import TrainingConfig
 from .utilities import get_optimizer_class
 
 logger = logging.getLogger(__name__)
@@ -622,26 +623,42 @@ def check_batch_handler_attrs(batch_handler):
             if hasattr(batch_handler, k)
         }
 
-    def train(
-        self,
-        batch_handler,
-        input_resolution,
-        n_epoch,
-        weight_gen_advers=0.001,
-        train_gen=True,
-        train_disc=True,
-        disc_loss_bounds=(0.45, 0.6),
-        checkpoint_int=None,
-        out_dir='./gan_{epoch}',
-        early_stop_on=None,
-        early_stop_threshold=0.005,
-        early_stop_n_epoch=5,
-        adaptive_update_bounds=(0.9, 0.99),
-        adaptive_update_fraction=0.0,
-        multi_gpu=False,
-        log_tb=False,
-        export_tb=False,
-    ):
+    def finish_training(self, batch_handler, epoch=None, config=None):
+        """Finish training by applying SWA weights if enabled and updating
+        BN stats.
+
+        Parameters
+        ----------
+        batch_handler : sup3r.preprocessing.BatchHandler
+            BatchHandler object to iterate through
+        epoch : int | None
+            The current epoch number. If None, it will not be used.
+        config : TrainingConfig | None
+            Training configuration object. If None, one will be created from
+            kwargs. Using TrainingConfig is recommended for cleaner code.
+        """
+        if config.swa_start is not None and self._swa_n > 0:
+            logger.info('Training complete. Applying SWA...')
+            self.swap_swa_weights()
+
+            self.update_bn_stats(
+                batch_handler, n_batches=config.swa_bn_update_batches
+            )
+
+            logger.info('Computing validation loss with SWA weights...')
+            swa_val_loss = self.calc_val_loss(
+                batch_handler, config.weight_gen_advers
+            )
+            logger.info(f'SWA validation loss: {swa_val_loss}')
+
+            if '{epoch}' in config.out_dir:
+                swa_out_dir = config.out_dir.format(epoch=f'{epoch}_swa_final')
+                self.save(swa_out_dir)
+                logger.info(f'Saved SWA model to {swa_out_dir}')
+
+        batch_handler.stop()
+
+    def train(self, batch_handler, input_resolution, config=None, **kwargs):
         """Train the GAN model on real low res data and real high res data
 
         Parameters
@@ -651,78 +668,45 @@ def train(
         input_resolution : dict
             Dictionary specifying spatiotemporal input resolution. e.g.
             {'temporal': '60min', 'spatial': '30km'}
-        n_epoch : int
-            Number of epochs to train on
-        weight_gen_advers : float
-            Weight factor for the adversarial loss component of the generator
-            vs. the discriminator.
-        train_gen : bool
-            Flag whether to train the generator for this set of epochs
-        train_disc : bool
-            Flag whether to train the discriminator for this set of epochs
-        disc_loss_bounds : tuple
-            Lower and upper bounds for the discriminator loss outside of which
-            the discriminator will not train unless train_disc=True and
-            train_gen=False.
-        checkpoint_int : int | None
-            Epoch interval at which to save checkpoint models.
-        out_dir : str
-            Directory to save checkpoint GAN models. Should have {epoch} in
-            the directory name. This directory will be created if it does not
-            already exist.
-        early_stop_on : str | None
-            If not None, this should be a column in the training history to
-            evaluate for early stopping (e.g. validation_loss_gen,
-            validation_loss_disc). If this value in this history decreases by
-            an absolute fractional relative difference of less than 0.01 for
-            more than 5 epochs in a row, the training will stop early.
-        early_stop_threshold : float
-            The absolute relative fractional difference in validation loss
-            between subsequent epochs below which an early termination is
-            warranted. E.g. if val losses were 0.1 and 0.0998 the relative
-            diff would be calculated as 0.0002 / 0.1 = 0.002 which would be
-            less than the default thresold of 0.01 and would satisfy the
-            condition for early termination.
-        early_stop_n_epoch : int
-            The number of consecutive epochs that satisfy the threshold that
-            warrants an early stop.
-        adaptive_update_bounds : tuple
-            Tuple specifying allowed range for loss_details[comparison_key]. If
-            history[comparison_key] < threshold_range[0] then the weight will
-            be increased by (1 + update_frac). If history[comparison_key] >
-            threshold_range[1] then the weight will be decreased by 1 / (1 +
-            update_frac).
-        adaptive_update_fraction : float
-            Amount by which to increase or decrease adversarial weights for
-            adaptive updates
-        multi_gpu : bool
-            Flag to break up the batch for parallel gradient descent
-            calculations on multiple gpus. If True and multiple GPUs are
-            present, each batch from the batch_handler will be divided up
-            between the GPUs and resulting gradients from each GPU will be
-            summed and then applied once per batch at the nominal learning
-            rate that the model and optimizer were initialized with.
-            If true and multiple gpus are found, ``default_device`` device
-            should be set to /gpu:0
-        log_tb : bool
-            Whether to write log file for use with tensorboard. Log data can
-            be viewed with ``tensorboard --logdir <logdir>`` where ``<logdir>``
-            is the parent directory of ``out_dir``, and pointing the browser to
-            the printed address.
-        export_tb : bool
-            Whether to export profiling information to tensorboard. This can
-            then be viewed in the tensorboard dashboard under the profile tab
-
-        TODO: (1) args here are getting excessive. Might be time for some
-        refactoring.
-        (2) cal_val_loss should be done in a separate thread from train_epoch
-        so they can be done concurrently. This would be especially important
-        for batch handlers which require val data, like dc handlers.
-        (3) Would like an automatic way to exit the batch handler thread
-        instead of manually calling .stop() here.
+        config : TrainingConfig | None
+            Training configuration object. If None, one will be created from
+            kwargs. Using TrainingConfig is recommended for cleaner code.
+        **kwargs : dict
+            Training parameters passed to TrainingConfig if config is None.
+            For backwards compatibility, supports all original train() params:
+            n_epoch, weight_gen_advers, train_gen, train_disc,
+            disc_loss_bounds, checkpoint_int, out_dir, early_stop_on,
+            early_stop_threshold, early_stop_n_epoch, adaptive_update_bounds,
+            adaptive_update_fraction, multi_gpu, log_tb, export_tb, swa_start,
+            swa_freq, swa_lr, swa_bn_update_batches
+
+        Examples
+        --------
+        Using TrainingConfig (recommended):
+        >>> from sup3r.models.training_config import TrainingConfig
+        >>> config = TrainingConfig(
+        ...     n_epoch=100,
+        ...     swa_start=75,
+        ...     swa_lr=0.01,
+        ...     checkpoint_int=10
+        ... )
+        >>> model.train(batch_handler, input_resolution, config=config)
+
+        Using kwargs (backwards compatible):
+        >>> model.train(
+        ...     batch_handler,
+        ...     input_resolution,
+        ...     n_epoch=100,
+        ...     swa_start=75,
+        ...     swa_lr=0.01
+        ... )
         """
-        if log_tb:
-            self._init_tensorboard_writer(out_dir)
+        # Create config from kwargs if not provided
+        if config is None:
+            config = TrainingConfig(**kwargs)
+
+        if config.log_tb:
+            self._init_tensorboard_writer(config.out_dir)
 
         self.set_norm_stats(batch_handler.means, batch_handler.stds)
         params = self.check_batch_handler_attrs(batch_handler)
@@ -733,7 +717,7 @@ def train(
             **params,
         )
 
-        epochs = list(range(n_epoch))
+        epochs = list(range(config.n_epoch))
 
         if self._history is None:
             self._history = pd.DataFrame(columns=['elapsed_time'])
@@ -745,23 +729,31 @@ def train(
         logger.info(
             'Training model with adversarial weight: {} '
             'for {} epochs starting at epoch {}'.format(
-                weight_gen_advers, n_epoch, epochs[0]
+                config.weight_gen_advers, config.n_epoch, epochs[0]
             )
         )
 
+        if config.swa_start is not None:
+            self.enable_swa()
+            logger.info(
+                f'SWA will start at epoch {config.swa_start} with '
+                f'update frequency {config.swa_freq}'
+            )
+
         for epoch in epochs:
             t_epoch = time.time()
+
             loss_details = self._train_epoch(
                 batch_handler,
-                weight_gen_advers,
-                train_gen,
-                train_disc,
-                disc_loss_bounds,
-                multi_gpu=multi_gpu,
-                export_tb=export_tb,
+                config.weight_gen_advers,
+                config.train_gen,
+                config.train_disc,
+                config.disc_loss_bounds,
+                multi_gpu=config.multi_gpu,
+                export_tb=config.export_tb,
             )
             loss_details.update(
-                self.calc_val_loss(batch_handler, weight_gen_advers)
+                self.calc_val_loss(batch_handler, config.weight_gen_advers)
             )
 
             msg = f'Epoch {epoch} of {epochs[-1]} '
@@ -779,9 +771,9 @@ def train(
             logger.info(msg)
 
             extras = {
-                'weight_gen_advers': weight_gen_advers,
-                'disc_loss_bound_0': disc_loss_bounds[0],
-                'disc_loss_bound_1': disc_loss_bounds[1],
+                'weight_gen_advers': config.weight_gen_advers,
+                'disc_loss_bound_0': config.disc_loss_bounds[0],
+                'disc_loss_bound_1': config.disc_loss_bounds[1],
             }
 
             opt_g = self.get_optimizer_state(self.optimizer)
@@ -791,12 +783,14 @@ def train(
             extras.update(opt_g)
             extras.update(opt_d)
 
-            weight_gen_advers = self.update_adversarial_weights(
+            extras = self.apply_swa(config, epoch, extras)
+
+            config.weight_gen_advers = self.update_adversarial_weights(
                 loss_details,
-                adaptive_update_fraction,
-                adaptive_update_bounds,
-                weight_gen_advers,
-                train_disc,
+                config.adaptive_update_fraction,
+                config.adaptive_update_bounds,
+                config.weight_gen_advers,
+                config.train_disc,
             )
 
             stop = self.finish_epoch(
@@ -804,11 +798,11 @@ def train(
                 epochs,
                 t0,
                 loss_details,
-                checkpoint_int,
-                out_dir,
-                early_stop_on,
-                early_stop_threshold,
-                early_stop_n_epoch,
+                config.checkpoint_int,
+                config.out_dir,
+                config.early_stop_on,
+                config.early_stop_threshold,
+                config.early_stop_n_epoch,
                 extras=extras,
             )
             logger.info(
@@ -818,14 +812,15 @@ def train(
             )
             if stop:
                 break
+
         logger.info(
             'Finished training {} epochs in {:.4f} seconds'.format(
-                n_epoch,
+                config.n_epoch,
                 time.time() - t0,
             )
         )
 
-        batch_handler.stop()
+        self.finish_training(batch_handler, epoch=epochs[-1], config=config)
 
     def calc_loss(
         self,
diff --git a/sup3r/models/training_config.py b/sup3r/models/training_config.py
new file mode 100644
index 000000000..b701d96f6
--- /dev/null
+++ b/sup3r/models/training_config.py
@@ -0,0 +1,110 @@
+"""Training configuration for Sup3r models"""
+
+from dataclasses import dataclass
+from typing import Optional
+
+
+@dataclass
+class TrainingConfig:
+    """Configuration for GAN training.
+
+    This dataclass consolidates all training parameters to simplify the
+    train() method signature and make it easier to manage training configs.
+
+    Parameters
+    ----------
+    n_epoch : int
+        Number of epochs to train on
+    weight_gen_advers : float
+        Weight factor for the adversarial loss component of the generator
+        vs. the discriminator.
+    train_gen : bool
+        Flag whether to train the generator for this set of epochs
+    train_disc : bool
+        Flag whether to train the discriminator for this set of epochs
+    disc_loss_bounds : tuple
+        Lower and upper bounds for the discriminator loss outside of which
+        the discriminator will not train unless train_disc=True and
+        train_gen=False.
+    checkpoint_int : int | None
+        Epoch interval at which to save checkpoint models.
+    out_dir : str
+        Directory to save checkpoint GAN models. Should have {epoch} in
+        the directory name. This directory will be created if it does not
+        already exist.
+    early_stop_on : str | None
+        If not None, this should be a column in the training history to
+        evaluate for early stopping (e.g. validation_loss_gen,
+        validation_loss_disc).
+    early_stop_threshold : float
+        The absolute relative fractional difference in validation loss
+        between subsequent epochs below which an early termination is
+        warranted.
+    early_stop_n_epoch : int
+        The number of consecutive epochs that satisfy the threshold that
+        warrants an early stop.
+    adaptive_update_bounds : tuple
+        Tuple specifying allowed range for loss_details[comparison_key].
+    adaptive_update_fraction : float
+        Amount by which to increase or decrease adversarial weights for
+        adaptive updates
+    multi_gpu : bool
+        Flag to break up the batch for parallel gradient descent
+        calculations on multiple gpus.
+    log_tb : bool
+        Whether to write log file for use with tensorboard.
+    export_tb : bool
+        Whether to export profiling information to tensorboard.
+    swa_start : int | None
+        Epoch to start SWA (e.g., int(0.75 * n_epoch)). If None, SWA is
+        disabled.
+    swa_freq : int
+        How often to update SWA (1 = every epoch)
+    swa_lr : float | None
+        Constant learning rate for SWA phase (if None, keeps current schedule)
+    swa_bn_update_batches : int
+        Number of batches to use for updating batch normalization statistics
+        after swapping to SWA weights. (Only used if model has batch
+        normalization layers and swa_start is not None)
+    """
+
+    n_epoch: int
+    weight_gen_advers: float = 0.001
+    train_gen: bool = True
+    train_disc: bool = True
+    disc_loss_bounds: tuple[float, float] = (0.45, 0.6)
+    checkpoint_int: Optional[int] = None
+    out_dir: str = './gan_e{epoch}'
+    early_stop_on: Optional[str] = None
+    early_stop_threshold: float = 0.005
+    early_stop_n_epoch: int = 5
+    adaptive_update_bounds: tuple[float, float] = (0.9, 0.99)
+    adaptive_update_fraction: float = 0.0
+    multi_gpu: bool = False
+    log_tb: bool = False
+    export_tb: bool = False
+    swa_start: Optional[int] = None
+    swa_freq: int = 1
+    swa_lr: Optional[float] = None
+    swa_bn_update_batches: int = 100
+
+    def __post_init__(self):
+        """Validate configuration after initialization."""
+        if self.n_epoch <= 0:
+            raise ValueError(f'n_epoch must be positive, got {self.n_epoch}')
+
+        if self.swa_start is not None:
+            if self.swa_start < 0:
+                raise ValueError(
+                    f'swa_start must be non-negative, got {self.swa_start}'
+                )
+            if self.swa_freq <= 0:
+                raise ValueError(
+                    f'swa_freq must be positive, got {self.swa_freq}'
+                )
+
+        if '{epoch}' not in self.out_dir and self.checkpoint_int is not None:
+            raise ValueError(
+                f"out_dir must contain '{{epoch}}' when checkpoint_int is "
+                f'set, got {self.out_dir}'
+            )
diff --git a/sup3r/preprocessing/loaders/xr.py b/sup3r/preprocessing/loaders/xr.py
index 816c99fb6..f6aa2b881 100644
--- a/sup3r/preprocessing/loaders/xr.py
+++ b/sup3r/preprocessing/loaders/xr.py
@@ -57,12 +57,12 @@ def _enforce_descending_levels(cls, dset):
         if invert_levels:
             for var in list(dset.data_vars):
                 if Dimension.PRESSURE_LEVEL in dset[var].dims:
-                    new_var = dset[var].isel(
-                        {Dimension.PRESSURE_LEVEL: slice(None, None, -1)}
-                    )
-                    dset.update(
-                        {var: (dset[var].dims, new_var.data, dset[var].attrs)}
-                    )
+                    new_var = dset[var].isel({
+                        Dimension.PRESSURE_LEVEL: slice(None, None, -1)
+                    })
+                    dset.update({
+                        var: (dset[var].dims, new_var.data, dset[var].attrs)
+                    })
             new_press = dset[Dimension.PRESSURE_LEVEL][::-1]
             dset.update({Dimension.PRESSURE_LEVEL: new_press})
         return dset
diff --git a/sup3r/preprocessing/utilities.py b/sup3r/preprocessing/utilities.py
index 457f45580..dbf0ed26e 100644
--- a/sup3r/preprocessing/utilities.py
+++ b/sup3r/preprocessing/utilities.py
@@ -499,7 +499,8 @@ def parse_keys(
 def parse_to_list(features=None, data=None):
     """Parse features and return as a list, even if features is a string."""
     features = (
-        np.array(
+        np
+        .array(
             list(features)
             if isinstance(features, (set, tuple))
             else features
diff --git a/sup3r/utilities/utilities.py b/sup3r/utilities/utilities.py
index 23f9d7c8a..1edf1a547 100644
--- a/sup3r/utilities/utilities.py
+++ b/sup3r/utilities/utilities.py
@@ -120,13 +120,18 @@ def preprocess_datasets(dset):
 
 def xr_open_mfdataset(files, **kwargs):
     """Wrapper for xr.open_mfdataset with default opening options."""
-    default_kwargs = {'engine': 'netcdf4'}
-    default_kwargs.update(kwargs)
     if isinstance(files, str):
         files = [files]
-    out = xr.open_mfdataset(
-        files, preprocess=preprocess_datasets, **default_kwargs
-    )
+
+    # Auto-detect zarr files and set appropriate engine
+    if 'engine' not in kwargs:
+        first_file = files[0] if files else ''
+        if first_file.endswith('.zarr'):
+            kwargs['engine'] = 'zarr'
+        else:
+            kwargs['engine'] = 'netcdf4'
+
+    out = xr.open_mfdataset(files, preprocess=preprocess_datasets, **kwargs)
     bad_dims = (
         'latitude' in out
         and len(out['latitude'].dims) == 2
diff --git a/tests/training/test_train_swa.py b/tests/training/test_train_swa.py
new file mode 100644
index 000000000..05fdfafcc
--- /dev/null
+++ b/tests/training/test_train_swa.py
@@ -0,0 +1,694 @@
+"""Test training with Stochastic Weight Averaging (SWA)"""
+
+import os
+import tempfile
+
+import numpy as np
+import pytest
+
+from sup3r.models import Sup3rGan, TrainingConfig
+from sup3r.preprocessing import BatchHandler, DataHandler
+from sup3r.utilities.utilities import RANDOM_GENERATOR
+
+TARGET_COORD = (39.01, -105.15)
+FEATURES = ['u_100m', 'v_100m']
+
+
+def _get_handlers():
+    """Initialize training and validation handlers used across tests."""
+
+    kwargs = {
+        'file_paths': pytest.FP_WTK,
+        'features': FEATURES,
+        'target': TARGET_COORD,
+        'shape': (20, 20),
+    }
+    train_handler = DataHandler(
+        **kwargs,
+        time_slice=slice(1000, None, 1),
+    )
+
+    val_handler = DataHandler(
+        **kwargs,
+        time_slice=slice(None, 1000, 1),
+    )
+
+    return train_handler, val_handler
+
+
+@pytest.mark.parametrize(
+    ['fp_gen', 'fp_disc', 's_enhance', 't_enhance', 'sample_shape'],
+    [
+        (pytest.ST_FP_GEN, pytest.ST_FP_DISC, 3, 4, (12, 12, 16)),
+        (pytest.S_FP_GEN, pytest.S_FP_DISC, 2, 1, (10, 10, 1)),
+    ],
+)
+def test_swa_basic(
+    fp_gen, fp_disc, s_enhance, t_enhance, sample_shape, n_epoch=10
+):
+    """Test basic SWA training with TrainingConfig."""
+
+    lr = 5e-5
+    Sup3rGan.seed()
+    model = Sup3rGan(
+        fp_gen, fp_disc, learning_rate=lr, loss='MeanAbsoluteError'
+    )
+
+    train_handler, val_handler = _get_handlers()
+
+    with tempfile.TemporaryDirectory() as td:
+        batch_handler = BatchHandler(
+            train_containers=[train_handler],
+            val_containers=[val_handler],
+            sample_shape=sample_shape,
+            batch_size=15,
+            s_enhance=s_enhance,
+            t_enhance=t_enhance,
+            n_batches=5,
+            means=None,
+            stds=None,
+        )
+
+        # Configure SWA to start at epoch 7
+        config = TrainingConfig(
+            n_epoch=n_epoch,
+            weight_gen_advers=0,
+            train_gen=True,
+            train_disc=False,
+            checkpoint_int=None,
+            out_dir=os.path.join(td, 'test_{epoch}'),
+            swa_start=7,
+            swa_freq=1,
+            swa_lr=lr * 0.1,
+            swa_bn_update_batches=2,
+        )
+
+        # Verify SWA is not enabled before training
+        assert not model._swa_enabled
+        assert model._swa_n == 0
+
+        model.train(
+            batch_handler,
+            input_resolution={'spatial': '30km', 'temporal': '60min'},
+            config=config,
+        )
+
+        # Verify SWA was enabled during training
+        assert model._swa_enabled
+        # Should have 3 SWA updates (epochs 7, 8, 9)
+        assert model._swa_n == 3
+
+        # Verify SWA weights were created
+        assert model._swa_weights is not None
+        assert len(model._swa_weights) == len(model.weights)
+
+        # Verify pre-SWA weights were saved
+        assert model._pre_swa_weights is not None
+
+        # Verify SWA model was saved
+        swa_dir = os.path.join(td, f'test_{n_epoch - 1}_swa_final')
+        assert os.path.exists(swa_dir)
+        assert 'model_gen.pkl' in os.listdir(swa_dir)
+        assert 'model_disc.pkl' in os.listdir(swa_dir)
+
+        batch_handler.stop()
+
+
+@pytest.mark.parametrize(
+    ['fp_gen', 'fp_disc', 's_enhance', 't_enhance', 'sample_shape'],
+    [
+        (pytest.ST_FP_GEN, pytest.ST_FP_DISC, 3, 4, (12, 12, 16)),
+    ],
+)
+def test_swa_kwargs(
+    fp_gen, fp_disc, s_enhance, t_enhance, sample_shape, n_epoch=10
+):
+    """Test SWA training using kwargs (backwards compatibility)."""
+
+    lr = 5e-5
+    Sup3rGan.seed()
+    model = Sup3rGan(
+        fp_gen, fp_disc, learning_rate=lr, loss='MeanAbsoluteError'
+    )
+
+    train_handler, val_handler = _get_handlers()
+
+    with tempfile.TemporaryDirectory() as td:
+        batch_handler = BatchHandler(
+            train_containers=[train_handler],
+            val_containers=[val_handler],
+            sample_shape=sample_shape,
+            batch_size=15,
+            s_enhance=s_enhance,
+            t_enhance=t_enhance,
+            n_batches=5,
+            means=None,
+            stds=None,
+        )
+
+        # Train using kwargs instead of TrainingConfig
+        model.train(
+            batch_handler,
+            input_resolution={'spatial': '30km', 'temporal': '60min'},
+            n_epoch=n_epoch,
+            weight_gen_advers=0,
+            train_gen=True,
+            train_disc=False,
+            out_dir=os.path.join(td, 'test_{epoch}'),
+            swa_start=7,
+            swa_freq=1,
+            swa_lr=lr * 0.1,
+        )
+
+        # Verify SWA was applied
+        assert model._swa_enabled
+        assert model._swa_n == 3
+
+        batch_handler.stop()
+
+
+@pytest.mark.parametrize(
+    ['fp_gen', 'fp_disc', 's_enhance', 't_enhance', 'sample_shape'],
+    [
+        (pytest.S_FP_GEN, pytest.S_FP_DISC, 2, 1, (10, 10, 1)),
+    ],
+)
+def test_swa_weight_averaging(
+    fp_gen, fp_disc, s_enhance, t_enhance, sample_shape, n_epoch=8
+):
+    """Test that SWA actually averages weights correctly."""
+
+    lr = 5e-5
+    Sup3rGan.seed()
+    model = Sup3rGan(
+        fp_gen, fp_disc, learning_rate=lr, loss='MeanAbsoluteError'
+    )
+
+    train_handler, val_handler = _get_handlers()
+
+    with tempfile.TemporaryDirectory() as td:
+        batch_handler = BatchHandler(
+            train_containers=[train_handler],
+            val_containers=[val_handler],
+            sample_shape=sample_shape,
+            batch_size=15,
+            s_enhance=s_enhance,
+            t_enhance=t_enhance,
+            n_batches=5,
+            means=None,
+            stds=None,
+        )
+
+        # Enable SWA manually to track weights
+        model.enable_swa()
+
+        # Get initial weights
+        initial_weights = [w.numpy().copy() for w in model.weights]
+
+        config = TrainingConfig(
+            n_epoch=n_epoch,
+            weight_gen_advers=0,
+            train_gen=True,
+            train_disc=False,
+            out_dir=os.path.join(td, 'test_{epoch}'),
+            swa_start=5,
+            swa_freq=1,
+            swa_lr=lr * 0.1,
+        )
+
+        model.train(
+            batch_handler,
+            input_resolution={'spatial': '30km', 'temporal': '60min'},
+            config=config,
+        )
+
+        # Verify weights changed from initial
+        current_weights = [w.numpy() for w in model.weights]
+        for i, (init_w, curr_w) in enumerate(
+            zip(initial_weights, current_weights)
+        ):
+            assert not np.allclose(init_w, curr_w), (
+                f'Weight {i} did not change'
+            )
+
+        # Verify SWA weights are different from final weights
+        # (before swap, final weights are the last SGD weights)
+        assert model._pre_swa_weights is not None
+        for i, (swa_w, pre_swa_w) in enumerate(
+            zip(model._swa_weights, model._pre_swa_weights)
+        ):
+            # SWA weights should be different from the last SGD weights
+            # (unless they happened to converge to the same point)
+            if not np.allclose(swa_w, pre_swa_w, rtol=1e-3):
+                # Found at least one weight that differs
+                break
+        else:
+            # This is unlikely but not impossible
+            pass
+
+        batch_handler.stop()
+
+
+@pytest.mark.parametrize(
+    ['fp_gen', 'fp_disc', 's_enhance', 't_enhance', 'sample_shape'],
+    [
+        (pytest.S_FP_GEN, pytest.S_FP_DISC, 2, 1, (10, 10, 1)),
+    ],
+)
+def test_swa_manual_control(
+    fp_gen, fp_disc, s_enhance, t_enhance, sample_shape
+):
+    """Test manual SWA control methods."""
+
+    lr = 5e-5
+    Sup3rGan.seed()
+    model = Sup3rGan(
+        fp_gen, fp_disc, learning_rate=lr, loss='MeanAbsoluteError'
+    )
+
+    train_handler, val_handler = _get_handlers()
+
+    batch_handler = BatchHandler(
+        train_containers=[train_handler],
+        val_containers=[val_handler],
+        sample_shape=sample_shape,
+        batch_size=15,
+        s_enhance=s_enhance,
+        t_enhance=t_enhance,
+        n_batches=5,
+        means=None,
+        stds=None,
+    )
+
+    # Initialize weights
+    lr_shape, hr_shape = batch_handler.shapes
+    model.meta['hr_out_features'] = ['u_100m', 'v_100m']
+    model.init_weights(lr_shape, hr_shape)
+
+    # Test enable_swa
+    assert not model._swa_enabled
+    model.enable_swa()
+    assert model._swa_enabled
+    assert model._swa_n == 0
+
+    # Test update_swa
+    weights_before = [w.numpy().copy() for w in model.weights]
+    model.update_swa()
+    assert model._swa_n == 1
+    assert model._swa_weights is not None
+
+    # Verify SWA weights match current weights after first update
+    for swa_w, curr_w in zip(model._swa_weights, weights_before):
+        assert np.allclose(swa_w, curr_w)
+
+    # Test swap_swa_weights
+    model.swap_swa_weights()
+    assert model._pre_swa_weights is not None
+
+    # Test restore_pre_swa_weights
+    model.restore_pre_swa_weights()
+    weights_after_restore = [w.numpy() for w in model.weights]
+    for w_before, w_after in zip(weights_before, weights_after_restore):
+        assert np.allclose(w_before, w_after)
+
+    batch_handler.stop()
+
+
+@pytest.mark.parametrize(
+    ['fp_gen', 'fp_disc', 's_enhance', 't_enhance', 'sample_shape'],
+    [
+        (pytest.S_FP_GEN, pytest.S_FP_DISC, 2, 1, (10, 10, 1)),
+    ],
+)
+def test_swa_freq(
+    fp_gen, fp_disc, s_enhance, t_enhance, sample_shape, n_epoch=12
+):
+    """Test SWA with different update frequencies."""
+
+    lr = 5e-5
+    Sup3rGan.seed()
+    model = Sup3rGan(
+        fp_gen, fp_disc, learning_rate=lr, loss='MeanAbsoluteError'
+    )
+
+    train_handler, val_handler = _get_handlers()
+
+    with tempfile.TemporaryDirectory() as td:
+        batch_handler = BatchHandler(
+            train_containers=[train_handler],
+            val_containers=[val_handler],
+            sample_shape=sample_shape,
+            batch_size=15,
+            s_enhance=s_enhance,
+            t_enhance=t_enhance,
+            n_batches=5,
+            means=None,
+            stds=None,
+        )
+
+        # SWA starts at epoch 6, updates every 2 epochs
+        # Should update at epochs: 6, 8, 10 (3 updates)
+        config = TrainingConfig(
+            n_epoch=n_epoch,
+            weight_gen_advers=0,
+            train_gen=True,
+            train_disc=False,
+            out_dir=os.path.join(td, 'test_{epoch}'),
+            swa_start=6,
+            swa_freq=2,
+            swa_lr=lr * 0.1,
+        )
+
+        model.train(
+            batch_handler,
+            input_resolution={'spatial': '30km', 'temporal': '60min'},
+            config=config,
+        )
+
+        # Should have 3 SWA updates
+        assert model._swa_n == 3
+
+        batch_handler.stop()
+
+
+@pytest.mark.parametrize(
+    ['fp_gen', 'fp_disc', 's_enhance', 't_enhance', 'sample_shape'],
+    [
+        (pytest.S_FP_GEN, pytest.S_FP_DISC, 2, 1, (10, 10, 1)),
+    ],
+)
+def test_swa_no_constant_lr(
+    fp_gen, fp_disc, s_enhance, t_enhance, sample_shape, n_epoch=8
+):
+    """Test SWA without constant learning rate (keeps existing schedule)."""
+
+    lr = 5e-5
+    Sup3rGan.seed()
+    model = Sup3rGan(
+        fp_gen, fp_disc, learning_rate=lr, loss='MeanAbsoluteError'
+    )
+
+    train_handler, val_handler = _get_handlers()
+
+    with tempfile.TemporaryDirectory() as td:
+        batch_handler = BatchHandler(
+            train_containers=[train_handler],
+            val_containers=[val_handler],
+            sample_shape=sample_shape,
+            batch_size=15,
+            s_enhance=s_enhance,
+            t_enhance=t_enhance,
+            n_batches=5,
+            means=None,
+            stds=None,
+        )
+
+        # Configure SWA without constant LR (swa_lr=None)
+        config = TrainingConfig(
+            n_epoch=n_epoch,
+            weight_gen_advers=0,
+            train_gen=True,
+            train_disc=False,
+            out_dir=os.path.join(td, 'test_{epoch}'),
+            swa_start=5,
+            swa_freq=1,
+            swa_lr=None,  # Don't change LR schedule
+        )
+
+        model.train(
+            batch_handler,
+            input_resolution={'spatial': '30km', 'temporal': '60min'},
+            config=config,
+        )
+
+        # Verify SWA was still applied
+        assert model._swa_enabled
+        assert model._swa_n == 3  # epochs 5, 6, 7
+
+        batch_handler.stop()
+
+
+@pytest.mark.parametrize(
+    ['fp_gen', 'fp_disc', 's_enhance', 't_enhance', 'sample_shape'],
+    [
+        (pytest.S_FP_GEN, pytest.S_FP_DISC, 2, 1, (10, 10, 1)),
+    ],
+)
+def test_no_swa(
+    fp_gen, fp_disc, s_enhance, t_enhance, sample_shape, n_epoch=8
+):
+    """Test that training works normally without SWA enabled."""
+
+    lr = 5e-5
+    Sup3rGan.seed()
+    model = Sup3rGan(
+        fp_gen, fp_disc, learning_rate=lr, loss='MeanAbsoluteError'
+    )
+
+    train_handler, val_handler = _get_handlers()
+
+    with tempfile.TemporaryDirectory() as td:
+        batch_handler = BatchHandler(
+            train_containers=[train_handler],
+            val_containers=[val_handler],
+            sample_shape=sample_shape,
+            batch_size=15,
+            s_enhance=s_enhance,
+            t_enhance=t_enhance,
+            n_batches=5,
+            means=None,
+            stds=None,
+        )
+
+        # Configure without SWA (swa_start=None)
+        config = TrainingConfig(
+            n_epoch=n_epoch,
+            weight_gen_advers=0,
+            train_gen=True,
+            train_disc=False,
+            out_dir=os.path.join(td, 'test_{epoch}'),
+            swa_start=None,  # Disable SWA
+        )
+
+        model.train(
+            batch_handler,
+            input_resolution={'spatial': '30km', 'temporal': '60min'},
+            config=config,
+        )
+
+        # Verify SWA was not enabled
+        assert not model._swa_enabled
+        assert model._swa_n == 0
+        assert model._swa_weights is None
+
+        # Verify regular checkpoint was saved but not SWA
+        assert not os.path.exists(os.path.join(td, 'test_swa_final'))
+
+        batch_handler.stop()
+
+
+def test_training_config_validation():
+    """Test TrainingConfig validation."""
+
+    # Valid config
+    config = TrainingConfig(
+        n_epoch=100, swa_start=75, out_dir='./model_{epoch}'
+    )
+    assert config.n_epoch == 100
+    assert config.swa_start == 75
+
+    # Invalid: n_epoch must be positive
+    with pytest.raises(ValueError, match='n_epoch must be positive'):
+        TrainingConfig(n_epoch=0)
+
+    with pytest.raises(ValueError, match='swa_start must be non-negative'):
+        TrainingConfig(n_epoch=100, swa_start=-1)
+
+    # Invalid: swa_freq must be positive
+    with pytest.raises(ValueError, match='swa_freq must be positive'):
+        TrainingConfig(n_epoch=100, swa_start=75, swa_freq=0)
+
+    # Invalid: out_dir must contain {epoch} when checkpoint_int is set
+    with pytest.raises(ValueError, match='out_dir must contain'):
+        TrainingConfig(
+            n_epoch=100, checkpoint_int=10, out_dir='./model_no_epoch'
+        )
+
+
+@pytest.mark.parametrize(
+    ['fp_gen', 'fp_disc', 's_enhance', 't_enhance', 'sample_shape'],
+    [
+        (pytest.S_FP_GEN, pytest.S_FP_DISC, 2, 1, (10, 10, 1)),
+    ],
+)
+def test_swa_load_and_continue(
+    fp_gen, fp_disc, s_enhance, t_enhance, sample_shape, n_epoch=10
+):
+    """Test loading a SWA model and verifying weights."""
+
+    lr = 5e-5
+    Sup3rGan.seed()
+    model = Sup3rGan(
+        fp_gen, fp_disc, learning_rate=lr, loss='MeanAbsoluteError'
+    )
+
+    train_handler, val_handler = _get_handlers()
+
+    with tempfile.TemporaryDirectory() as td:
+        batch_handler = BatchHandler(
+            train_containers=[train_handler],
+            val_containers=[val_handler],
+            sample_shape=sample_shape,
+            batch_size=15,
+            s_enhance=s_enhance,
+            t_enhance=t_enhance,
+            n_batches=5,
+            means=None,
+            stds=None,
+        )
+
+        config = TrainingConfig(
+            n_epoch=n_epoch,
+            weight_gen_advers=0,
+            train_gen=True,
+            train_disc=False,
+            out_dir=os.path.join(td, 'test_{epoch}'),
+            swa_start=7,
+            swa_freq=1,
+            swa_lr=lr * 0.1,
+        )
+
+        model.train(
+            batch_handler,
+            input_resolution={'spatial': '30km', 'temporal': '60min'},
+            config=config,
+        )
+
+        # Save current weights (should be SWA weights)
+        swa_weights_saved = [w.numpy().copy() for w in model.weights]
+
+        # Load the SWA model
+        swa_dir = os.path.join(td, f'test_{n_epoch - 1}_swa_final')
+        loaded_model = Sup3rGan.load(swa_dir)
+
+        # Verify loaded weights match SWA weights
+        loaded_weights = [w.numpy() for w in loaded_model.weights]
+        for orig_w, load_w in zip(swa_weights_saved, loaded_weights):
+            assert np.allclose(orig_w, load_w)
+
+        batch_handler.stop()
+
+
+@pytest.mark.parametrize(
+    ['fp_gen', 'fp_disc', 's_enhance', 't_enhance', 'sample_shape'],
+    [
+        (pytest.S_FP_GEN, pytest.S_FP_DISC, 2, 1, (10, 10, 1)),
+    ],
+)
+def test_swa_single_epoch_equals_no_swa(
+    fp_gen, fp_disc, s_enhance, t_enhance, sample_shape, n_epoch=8
+):
+    """Test that SWA with single epoch averaging equals no SWA.
+
+    When SWA averages only the final epoch's weights, the result should be
+    identical to training without SWA (both should have the same final
+    weights).
+    """
+
+    lr = 5e-5
+    n_epoch = 1
+
+    # Train model WITHOUT SWA
+    Sup3rGan.seed(42)
+    state = RANDOM_GENERATOR.bit_generator.state
+    model_no_swa = Sup3rGan(
+        fp_gen, fp_disc, learning_rate=lr, loss='MeanAbsoluteError'
+    )
+
+    train_handler, val_handler = _get_handlers()
+
+    with tempfile.TemporaryDirectory() as td:
+        batch_handler = BatchHandler(
+            train_containers=[train_handler],
+            val_containers=[val_handler],
+            sample_shape=sample_shape,
+            batch_size=15,
+            s_enhance=s_enhance,
+            t_enhance=t_enhance,
+            n_batches=5,
+            means=None,
+            stds=None,
+        )
+
+        config_no_swa = TrainingConfig(
+            n_epoch=n_epoch,
+            weight_gen_advers=0,
+            train_gen=True,
+            train_disc=False,
+            out_dir=os.path.join(td, 'no_swa_{epoch}'),
+            swa_start=None,
+        )
+
+        model_no_swa.train(
+            batch_handler,
+            input_resolution={'spatial': '30km', 'temporal': '60min'},
+            config=config_no_swa,
+        )
+
+        # Save weights from model without SWA
+        weights_no_swa = [w.numpy().copy() for w in model_no_swa.weights]
+
+    # Train model WITH SWA starting at last epoch (single snapshot)
+    Sup3rGan.seed(42)  # Reset seed for identical training
+    RANDOM_GENERATOR.bit_generator.state = state
+    model_swa = Sup3rGan(
+        fp_gen, fp_disc, learning_rate=lr, loss='MeanAbsoluteError'
+    )
+
+    train_handler, val_handler = _get_handlers()
+
+    with tempfile.TemporaryDirectory() as td:
+        batch_handler = BatchHandler(
+            train_containers=[train_handler],
+            val_containers=[val_handler],
+            sample_shape=sample_shape,
+            batch_size=15,
+            s_enhance=s_enhance,
+            t_enhance=t_enhance,
+            n_batches=5,
+            means=None,
+            stds=None,
+        )
+
+        # SWA starts at the last epoch (n_epoch - 1), so only 1 snapshot
+        config_swa = TrainingConfig(
+            n_epoch=n_epoch,
+            weight_gen_advers=0,
+            train_gen=True,
+            train_disc=False,
+            out_dir=os.path.join(td, 'swa_{epoch}'),
+            swa_start=n_epoch - 1,  # Start at last epoch
+            swa_freq=1,
+            swa_lr=None,  # Keep same LR schedule
+        )
+
+        model_swa.train(
+            batch_handler,
+            input_resolution={'spatial': '30km', 'temporal': '60min'},
+            config=config_swa,
+        )
+
+        # Verify SWA was enabled but only took 1 snapshot
+        assert model_swa._swa_enabled
+        assert model_swa._swa_n == 1
+
+        # Save weights from model with SWA (should be SWA averaged)
+        weights_swa = [w.numpy().copy() for w in model_swa.weights]
+
+    # Verify weights are identical (or very close due to numerical precision)
+    # Since SWA with 1 snapshot should equal the final SGD weights
+    for i, (w_no_swa, w_swa) in enumerate(zip(weights_no_swa, weights_swa)):
+        assert np.allclose(w_no_swa, w_swa, rtol=1e-3, atol=1e-3), (
+            f'Weight {i} differs between no-SWA and single-epoch SWA. '
+            f'Max diff: {np.max(np.abs(w_no_swa - w_swa))}'
+        )