diff --git a/doc/source/whatsnew/v2.0.1.rst b/doc/source/whatsnew/v2.0.1.rst
index 4fda2cd11ce12..33ce62415ea0f 100644
--- a/doc/source/whatsnew/v2.0.1.rst
+++ b/doc/source/whatsnew/v2.0.1.rst
@@ -21,6 +21,7 @@ Fixed regressions
 Bug fixes
 ~~~~~~~~~
 - Fixed bug in :func:`merge` when merging with ``ArrowDtype`` one one and a NumPy dtype on the other side (:issue:`52406`)
+- Bug in :meth:`Series.describe` not returning :class:`ArrowDtype` with ``pyarrow.float64`` type with numeric data (:issue:`52427`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_201.other:
diff --git a/pandas/core/methods/describe.py b/pandas/core/methods/describe.py
index e9f1eaabbe246..45cf038ebc19e 100644
--- a/pandas/core/methods/describe.py
+++ b/pandas/core/methods/describe.py
@@ -36,6 +36,7 @@
     is_timedelta64_dtype,
 )
 
+from pandas.core.arrays.arrow.dtype import ArrowDtype
 from pandas.core.arrays.floating import Float64Dtype
 from pandas.core.reshape.concat import concat
 
@@ -229,7 +230,12 @@ def describe_numeric_1d(series: Series, percentiles: Sequence[float]) -> Series:
     # GH#48340 - always return float on non-complex numeric data
     dtype: DtypeObj | None
     if is_extension_array_dtype(series.dtype):
-        dtype = Float64Dtype()
+        if isinstance(series.dtype, ArrowDtype):
+            import pyarrow as pa
+
+            dtype = ArrowDtype(pa.float64())
+        else:
+            dtype = Float64Dtype()
     elif is_numeric_dtype(series.dtype) and not is_complex_dtype(series.dtype):
         dtype = np.dtype("float")
     else:
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index df470d85a4fad..31d4c76a8db11 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -2387,3 +2387,16 @@ def test_setitem_boolean_replace_with_mask_segfault():
     expected = arr.copy()
     arr[np.zeros((N,), dtype=np.bool_)] = False
     assert arr._pa_array == expected._pa_array
+
+
+@pytest.mark.parametrize("pa_type", tm.ALL_INT_PYARROW_DTYPES + tm.FLOAT_PYARROW_DTYPES)
+def test_describe_numeric_data(pa_type):
+    # GH 52470
+    data = pd.Series([1, 2, 3], dtype=ArrowDtype(pa_type))
+    result = data.describe()
+    expected = pd.Series(
+        [3, 2, 1, 1, 1.5, 2.0, 2.5, 3],
+        dtype=ArrowDtype(pa.float64()),
+        index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+    )
+    tm.assert_series_equal(result, expected)