diff --git a/modulus/datapipes/healpix/data_modules.py b/modulus/datapipes/healpix/data_modules.py
index fdabb9f8d..665dc77bf 100644
--- a/modulus/datapipes/healpix/data_modules.py
+++ b/modulus/datapipes/healpix/data_modules.py
@@ -613,6 +613,9 @@ def setup(self) -> None:
                     batch_size=self.batch_size,
                 )
 
+        if self.constants is not None:
+            dataset = dataset.sel(channel_c=list(self.constants.values()))
+
         if self.splits is not None and self.forecast_init_times is None:
             self.train_dataset = TimeSeriesDataset(
                 dataset.sel(
@@ -1033,6 +1036,9 @@ def setup(self) -> None:
                     batch_size=self.batch_size,
                 )
 
+        if self.constants is not None:
+            dataset = dataset.sel(channel_c=list(self.constants.values()))
+
         if self.splits is not None and self.forecast_init_times is None:
             self.train_dataset = CoupledTimeSeriesDataset(
                 dataset.sel(
diff --git a/test/datapipes/test_healpix.py b/test/datapipes/test_healpix.py
index 73216be18..3667cb650 100644
--- a/test/datapipes/test_healpix.py
+++ b/test/datapipes/test_healpix.py
@@ -550,7 +550,10 @@ def test_TimeSeriesDataModule_get_constants(
     # open our test dataset
     ds_path = Path(data_dir, dataset_name + ".zarr")
     zarr_ds = xr.open_zarr(ds_path)
-    expected = np.transpose(zarr_ds.constants.values, axes=(1, 0, 2, 3))
+    expected = np.transpose(
+        zarr_ds.constants.sel(channel_c=list(constants.keys())).values,
+        axes=(1, 0, 2, 3),
+    )
 
     assert np.array_equal(
         timeseries_dm.get_constants(),
diff --git a/test/datapipes/test_healpix_couple.py b/test/datapipes/test_healpix_couple.py
index 833827c9e..681a943bf 100644
--- a/test/datapipes/test_healpix_couple.py
+++ b/test/datapipes/test_healpix_couple.py
@@ -759,7 +759,10 @@ def test_CoupledTimeSeriesDataModule_get_constants(
     # open our test dataset
     ds_path = Path(data_dir, dataset_name + ".zarr")
     zarr_ds = xr.open_zarr(ds_path)
-    expected = np.transpose(zarr_ds.constants.values, axes=(1, 0, 2, 3))
+    expected = np.transpose(
+        zarr_ds.constants.sel(channel_c=list(constants.keys())).values,
+        axes=(1, 0, 2, 3),
+    )
 
     assert np.array_equal(
         timeseries_dm.get_constants(),