Raise error when datetime64 or timedelta64 values that are outside the valid range for ns precision are converted to ns precision

doc/whats-new.rst

@@ -34,6 +34,8 @@ New Features
 Bug fixes
 ~~~~~~~~~
 
+- Fix bug where datetime64 times are silently changed to incorrect values if they are outside the valid date range for ns precision when provided in some other units (:issue:`4427`, :pull:`4454`).
+  By `Andrew Pauling <https://github.com/andrewpauling>`_
 - Fix silently overwriting the `engine` key when passing :py:func:`open_dataset` a file object
   to an incompatible netCDF (:issue:`4457`). Now incompatible combinations of files and engines raise
   an exception instead. By `Alessandro Amici <https://github.com/alexamici>`_.

xarray/core/parallel.py

@@ -255,7 +255,9 @@ def map_blocks(
     to the function being applied in ``xr.map_blocks()``:
 
     >>> array.map_blocks(
-    ...     calculate_anomaly, kwargs={"groupby_type": "time.year"}, template=array,
+    ...     calculate_anomaly,
+    ...     kwargs={"groupby_type": "time.year"},
+    ...     template=array,
     ... )  # doctest: +ELLIPSIS
     <xarray.DataArray (time: 24)>
     dask.array<calculate_anomaly-...-<this, shape=(24,), dtype=float64, chunksize=(24,), chunktype=numpy.ndarray>

xarray/core/variable.py

@@ -177,7 +177,9 @@ def _maybe_wrap_data(data):
 
 def _possibly_convert_objects(values):
     """Convert arrays of datetime.datetime and datetime.timedelta objects into
-    datetime64 and timedelta64, according to the pandas convention.
+    datetime64 and timedelta64, according to the pandas convention. Also used for
+    validating that datetime64 and timedelta64 objects are within the valid date
+    range for ns precision, as pandas will raise an error if they are not.
     """
     return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
 
@@ -238,16 +240,16 @@ def as_compatible_data(data, fastpath=False):
                     '"1"'
                 )
 
-    # validate whether the data is valid data types
+    # validate whether the data is valid data types.
     data = np.asarray(data)
 
     if isinstance(data, np.ndarray):
         if data.dtype.kind == "O":
             data = _possibly_convert_objects(data)
         elif data.dtype.kind == "M":
-            data = np.asarray(data, "datetime64[ns]")
+            data = _possibly_convert_objects(data)
         elif data.dtype.kind == "m":
-            data = np.asarray(data, "timedelta64[ns]")
+            data = _possibly_convert_objects(data)
 
     return _maybe_wrap_data(data)
 

xarray/tests/test_variable.py

@@ -294,6 +294,19 @@ def test_object_conversion(self):
         actual = self.cls("x", data)
         assert actual.dtype == data.dtype
 
+    def test_datetime64_valid_range(self):
+        data = np.datetime64("1250-01-01", "us")
+        pderror = pd.errors.OutOfBoundsDatetime
+        with raises_regex(pderror, "Out of bounds nanosecond"):
+            self.cls(["t"], [data])
+
+    @pytest.mark.xfail(reason="pandas issue 36615")
+    def test_timedelta64_valid_range(self):
+        data = np.timedelta64("200000", "D")
+        pderror = pd.errors.OutOfBoundsTimedelta
+        with raises_regex(pderror, "Out of bounds nanosecond"):
+            self.cls(["t"], [data])
+
     def test_pandas_data(self):
         v = self.cls(["x"], pd.Series([0, 1, 2], index=[3, 2, 1]))
         assert_identical(v, v[[0, 1, 2]])