Add clear error message when no complete seasons are found

tests/test_temporal.py

@@ -762,6 +762,21 @@ def test_raises_error_with_incorrect_custom_seasons_argument(self):
                 season_config={"custom_seasons": custom_seasons},
             )
 
+    def test_raises_error_with_dataset_that_has_no_complete_seasons(self):
+        ds = self.ds.copy()
+        ds = ds.isel(time=slice(0, 1))
+        custom_seasons = [["Dec", "Jan"]]
+
+        with pytest.raises(RuntimeError):
+            ds.temporal.group_average(
+                "ts",
+                "season",
+                season_config={
+                    "custom_seasons": custom_seasons,
+                    "drop_incomplete_seasons": True,
+                },
+            )
+
     def test_weighted_custom_seasonal_averages(self):
         ds = self.ds.copy()
 

xcdat/temporal.py

@@ -249,7 +249,7 @@ def group_average(
         Time bounds are used for generating weights to calculate weighted group
         averages (refer to the ``weighted`` parameter documentation below).
 
-        .. deprecated:: v0.7.0
+        .. deprecated:: v0.8.0
             The ``season_config`` dictionary argument ``"drop_incomplete_djf"``
             is being deprecated. Please use ``"drop_incomplete_seasons"``
             instead.
@@ -1207,7 +1207,14 @@ def _drop_incomplete_seasons(self, ds: xr.Dataset) -> xr.Dataset:
         # Get the incomplete seasons and drop the time coordinates that are in
         # those incomplete seasons.
         indexes_to_drop = df[df["expected_months"] != df["actual_months"]].index
-        if len(indexes_to_drop) > 0:
+
+        if len(indexes_to_drop) == len(time_coords):
+            raise RuntimeError(
+                "No time coordinates remain with `drop_incomplete_seasons=True`. "
+                "Check the dataset has at least one complete season and/or "
+                "specify `drop_incomplete_seasons=False` instead."
+            )
+        elif len(indexes_to_drop) > 0:
             # The dataset needs to be split into a dataset with and a dataset
             # without the time dimension because the xarray `.where()` method
             # concatenates the time dimension to non-time dimension data vars,