BUG: ArrowExtensionArray.fillna with duration types

pandas/core/arrays/arrow/array.py

@@ -381,8 +381,7 @@ def _box_pa_scalar(cls, value, pa_type: pa.DataType | None = None) -> pa.Scalar:
         elif isna(value):
             pa_scalar = pa.scalar(None, type=pa_type)
         else:
-            # GH 53171: pyarrow does not yet handle pandas non-nano correctly
-            # see https://github.com/apache/arrow/issues/33321
+            # Workaround https://github.com/apache/arrow/issues/37291
             if isinstance(value, Timedelta):
                 if pa_type is None:
                     pa_type = pa.duration(value.unit)
@@ -448,8 +447,7 @@ def _box_pa_array(
                 and pa.types.is_duration(pa_type)
                 and (not isinstance(value, np.ndarray) or value.dtype.kind not in "mi")
             ):
-                # GH 53171: pyarrow does not yet handle pandas non-nano correctly
-                # see https://github.com/apache/arrow/issues/33321
+                # Workaround https://github.com/apache/arrow/issues/37291
                 from pandas.core.tools.timedeltas import to_timedelta
 
                 value = to_timedelta(value, unit=pa_type.unit).as_unit(pa_type.unit)
@@ -462,8 +460,7 @@ def _box_pa_array(
                 pa_array = pa.array(value, from_pandas=True)
 
             if pa_type is None and pa.types.is_duration(pa_array.type):
-                # GH 53171: pyarrow does not yet handle pandas non-nano correctly
-                # see https://github.com/apache/arrow/issues/33321
+                # Workaround https://github.com/apache/arrow/issues/37291
                 from pandas.core.tools.timedeltas import to_timedelta
 
                 value = to_timedelta(value)
@@ -965,26 +962,11 @@ def fillna(
                     f" expected {len(self)}"
                 )
 
-        def convert_fill_value(value, pa_type, dtype):
-            if value is None:
-                return value
-            if isinstance(value, (pa.Scalar, pa.Array, pa.ChunkedArray)):
-                return value
-            if isinstance(value, Timedelta) and value.unit in ("s", "ms"):
-                # Workaround https://github.com/apache/arrow/issues/37291
-                value = value.to_numpy()
-            if is_array_like(value):
-                pa_box = pa.array
-            else:
-                pa_box = pa.scalar
-            try:
-                value = pa_box(value, type=pa_type, from_pandas=True)
-            except pa.ArrowTypeError as err:
-                msg = f"Invalid value '{str(value)}' for dtype {dtype}"
-                raise TypeError(msg) from err
-            return value
-
-        fill_value = convert_fill_value(value, self._pa_array.type, self.dtype)
+        try:
+            fill_value = self._box_pa(value, pa_type=self._pa_array.type)
+        except pa.ArrowTypeError as err:
+            msg = f"Invalid value '{str(value)}' for dtype {self.dtype}"
+            raise TypeError(msg) from err
 
         try:
             if method is None:

pandas/tests/extension/test_arrow.py

@@ -2997,3 +2997,13 @@ def test_arrowextensiondtype_dataframe_repr():
     # pyarrow.ExtensionType values are displayed
     expected = "     col\n0  15340\n1  15341\n2  15342"
     assert result == expected
+
+
+@pytest.mark.parametrize("pa_type", tm.TIMEDELTA_PYARROW_DTYPES)
+def test_duration_fillna_numpy(pa_type):
+    # GH 54707
+    ser1 = pd.Series([None, 2], dtype=ArrowDtype(pa_type))
+    ser2 = pd.Series(np.array([1, 3], dtype=f"m8[{pa_type.unit}]"))
+    result = ser1.fillna(ser2)
+    expected = pd.Series([1, 2], dtype=ArrowDtype(pa_type))
+    tm.assert_series_equal(result, expected)