diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index 005818b0779e6..e614df1a33905 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -65,6 +65,7 @@ Other enhancements
 - Support passing a :class:`Iterable[Hashable]` input to :meth:`DataFrame.drop_duplicates` (:issue:`59237`)
 - Support reading Stata 102-format (Stata 1) dta files (:issue:`58978`)
 - Support reading Stata 110-format (Stata 7) dta files (:issue:`47176`)
+- Support skipna=True in operations on Float64 arrays with null values (:issue:`59965`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.notable_bug_fixes:
diff --git a/pandas/core/array_algos/masked_reductions.py b/pandas/core/array_algos/masked_reductions.py
index bdf88f2e9fa07..105e825712f5c 100644
--- a/pandas/core/array_algos/masked_reductions.py
+++ b/pandas/core/array_algos/masked_reductions.py
@@ -12,6 +12,9 @@
 
 from pandas._libs import missing as libmissing
 
+from pandas.core.dtypes.common import is_float_dtype
+
+from pandas.core.missing import isna
 from pandas.core.nanops import check_below_min_count
 
 if TYPE_CHECKING:
@@ -57,6 +60,9 @@ def _reductions(
         else:
             return func(values, axis=axis, **kwargs)
     else:
+        if is_float_dtype(values):
+            mask |= isna(values)
+
         if check_below_min_count(values.shape, mask, min_count) and (
             axis is None or values.ndim == 1
         ):
diff --git a/pandas/tests/series/test_reductions.py b/pandas/tests/series/test_reductions.py
index 86ce60b1fc12b..22b1152641c42 100644
--- a/pandas/tests/series/test_reductions.py
+++ b/pandas/tests/series/test_reductions.py
@@ -208,3 +208,11 @@ def test_median_with_convertible_string_raises():
     df = ser.to_frame()
     with pytest.raises(TypeError, match=msg):
         df.median()
+
+
+def test_mean_with_skipna():
+    # GH#59965 skipna=True operations don't skip NaN in FloatingArrays
+    series1 = Series({"a": 0.0, "b": 1, "c": 1}, dtype="Float64")
+    series2 = Series({"a": 0.0, "b": 2, "c": 2}, dtype="Float64")
+    result = series1 / series2
+    assert np.isclose(result.mean(skipna=True), 0.5)