diff --git a/‎doc/source/whatsnew/v3.0.6.rst‎
Lines changed: 1 addition & 0 deletions b/‎doc/source/whatsnew/v3.0.6.rst‎
Lines changed: 1 addition & 0 deletions
diff --git a/‎pandas/core/arrays/arrow/array.py‎
Lines changed: 5 additions & 12 deletions b/‎pandas/core/arrays/arrow/array.py‎
Lines changed: 5 additions & 12 deletions
diff --git a/‎pandas/tests/extension/test_arrow.py‎
Lines changed: 19 additions & 1 deletion b/‎pandas/tests/extension/test_arrow.py‎
Lines changed: 19 additions & 1 deletion
@@ -19,6 +19,7 @@ Fixed regressions
 - Bug in :meth:`Timestamp.tz_convert` and :meth:`Timestamp.tz_localize` and their :class:`Index` and :class:`Series` counterparts returning a UTC offset a few minutes off for dates before the specified timezone adopted a standard time (typically early 20th century); this only affected older systems whose ``tzdata`` was compiled with a toolchain from between 2013 and 2018, such as Amazon Linux 2 and RHEL 8 and its derivatives (:issue:`67066`)
 - Fixed bug in :func:`read_csv` with the ``c`` engine and ``low_memory=True`` where a mixed-dtype column selected by ``usecols`` raised ``IndexError`` instead of emitting a :class:`DtypeWarning` unless it was the first column in the file, and where that warning named the wrong column when the file had an index column (:issue:`67375`)
 - Fixed bug in :func:`read_csv` with the ``c`` engine and ``low_memory=True`` where the number reported next to each column name in the mixed-type :class:`DtypeWarning` was a running count of the warned columns rather than the column's position in the file (:issue:`67375`)
+- Regression in :meth:`Series.interpolate` with ``method="linear"`` and a pyarrow-backed dtype leaving consecutive and trailing missing values unfilled, and truncating interpolated values for integer dtypes; the result now matches the equivalent masked (e.g. ``Int64``) dtype, including the upcast to ``float64[pyarrow]`` (:issue:`65345`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_306.bug_fixes:
 
@@ -2507,18 +2507,11 @@ def interpolate(
         if not self.dtype._is_numeric:
             raise TypeError(f"Cannot interpolate with {self.dtype} dtype")
 
-        if (
-            method == "linear"
-            and limit_area is None
-            and limit is None
-            and limit_direction == "forward"
-        ):
-            values = self._pa_array.combine_chunks()
-            na_value = pa.array([None], type=values.type)
-            y_diff_2 = pc.fill_null_backward(pc.pairwise_diff_checked(values, period=2))
-            prev_values = pa.concat_arrays([na_value, values[:-2], na_value])
-            interps = pc.add_checked(prev_values, pc.divide_checked(y_diff_2, 2))
-            return self._from_pyarrow_array(pc.coalesce(self._pa_array, interps))
+        # GH#65345: a pyarrow-native fast path for
+        # method="linear"/limit_direction="forward" was removed here because
+        # it only handled isolated NAs (leaving consecutive and trailing NAs
+        # unfilled), truncated interpolated values for integer dtypes, and
+        # did not upcast to float64 like the general path below.
 
         mask = self.isna()
         if self.dtype.kind == "f":
 
@@ -3705,9 +3705,27 @@ def test_interpolate_not_numeric(data):
 
 @pytest.mark.parametrize("dtype", ["int64[pyarrow]", "float64[pyarrow]"])
 def test_interpolate_linear(dtype):
+    # GH#65345 results should match the masked (e.g. Int64) dtypes:
+    # upcast to float, and fill the trailing NA going forward
     ser = pd.Series([None, 1, 2, None, 4, None], dtype=dtype)
     result = ser.interpolate()
-    expected = pd.Series([None, 1, 2, 3, 4, None], dtype=dtype)
+    expected = pd.Series([None, 1.0, 2.0, 3.0, 4.0, 4.0], dtype="float64[pyarrow]")
+    tm.assert_series_equal(result, expected)
+
+
+def test_interpolate_linear_consecutive_na():
+    # GH#65345 consecutive interior NAs were left unfilled
+    ser = pd.Series([1, 2, 3, None, None, 6, 7], dtype="int64[pyarrow]")
+    result = ser.interpolate(method="linear", limit_direction="forward")
+    expected = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], dtype="float64[pyarrow]")
+    tm.assert_series_equal(result, expected)
+
+
+def test_interpolate_linear_int_fractional():
+    # GH#65345 result should not truncate the interpolated value (1 instead of 1.5)
+    ser = pd.Series([1, None, 2], dtype="int64[pyarrow]")
+    result = ser.interpolate(method="linear")
+    expected = pd.Series([1.0, 1.5, 2.0], dtype="float64[pyarrow]")
     tm.assert_series_equal(result, expected)