Avoid large chunks in climate_statistics preprocessor function with…

… `period='full'` (#2404)

esmvalcore/preprocessor/_time.py

@@ -412,7 +412,12 @@ def get_time_weights(cube: Cube) -> np.ndarray | da.core.Array:
 
     # Extract 1D time weights (= lengths of time intervals)
     time_weights = time.lazy_bounds()[:, 1] - time.lazy_bounds()[:, 0]
-    if not cube.has_lazy_data():
+    if cube.has_lazy_data():
+        # Align the weight chunks with the data chunks to avoid excessively
+        # large chunks as a result of broadcasting.
+        time_chunks = cube.lazy_data().chunks[coord_dims[0]]
+        time_weights = time_weights.rechunk(time_chunks)
+    else:
         time_weights = time_weights.compute()
     return time_weights
 

tests/unit/preprocessor/_time/test_time.py

@@ -1777,10 +1777,11 @@ def test_get_time_weights():
 def test_get_time_weights_lazy():
     """Test ``get_time_weights`` for complex cube with lazy data."""
     cube = _make_cube()
-    cube.data = cube.lazy_data()
+    cube.data = cube.lazy_data().rechunk((1, 1, 1, 3))
     weights = get_time_weights(cube)
     assert isinstance(weights, da.Array)
     assert weights.shape == (2, )
+    assert weights.chunks == ((1, 1), )
     np.testing.assert_allclose(weights, [15.0, 30.0])