diff --git a/CHANGELOG.md b/CHANGELOG.md
index 577e1cc2..ac22bf04 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -12,6 +12,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * `irfft` with `out=` now writes directly into `out` when the cast or zero-padded copy of a non-contiguous input has the same memory order as `out`, avoiding a temporary array and an extra copy [gh-396](https://github.com/IntelPython/mkl_fft/pull/396)
 
 ### Fixed
+* Zero-padding (`n` larger than the input length along the axis) of input that is both C- and F-contiguous, such as shape `(m, 1)` padded along axis 1, now gives a C-contiguous result as NumPy does instead of a Fortran-contiguous one, so a C-ordered `out` is also written directly without a temporary copy [gh-397](https://github.com/IntelPython/mkl_fft/pull/397)
 
 ## [2.4.0] - 2026-10-16
 
diff --git a/mkl_fft/_pydfti.pyx b/mkl_fft/_pydfti.pyx
index 77936f31..41f7bdec 100644
--- a/mkl_fft/_pydfti.pyx
+++ b/mkl_fft/_pydfti.pyx
@@ -229,9 +229,9 @@ cdef cnp.ndarray _pad_array(
         b_shape[axis] = n
 
         # allocating temporary buffer
-        x_arr_is_fortran = cnp.PyArray_CHKFLAGS(
-            x_arr, cnp.NPY_ARRAY_F_CONTIGUOUS
-        )
+        # F order only if x_arr is not also C-contiguous (e.g. shape (m, 1)),
+        # as NumPy does, otherwise padding along a unit axis gives an F array
+        x_arr_is_fortran = cnp.PyArray_ISFORTRAN(x_arr)
         b_arr = <cnp.ndarray> cnp.PyArray_EMPTY(
             b_ndim, b_shape, <cnp.NPY_TYPES> b_type, x_arr_is_fortran
         )  # 0 for C-contiguous
diff --git a/mkl_fft/tests/test_fft1d.py b/mkl_fft/tests/test_fft1d.py
index e63421e3..0c09df37 100644
--- a/mkl_fft/tests/test_fft1d.py
+++ b/mkl_fft/tests/test_fft1d.py
@@ -522,6 +522,28 @@ def test_irfft_dtype(dt):
     assert_allclose(result, expected, rtol=1e-7, atol=1e-7, strict=True)
 
 
+@pytest.mark.parametrize(
+    "shape, axis", [((5, 1), 1), ((1, 5), 0), ((4, 1, 1), 2), ((1, 1, 4), 0)]
+)
+@pytest.mark.parametrize("func", ["fft", "ifft", "rfft", "irfft"])
+def test_pad_both_contiguous_input(func, shape, axis):
+    # x is both C- and F-contiguous and is zero-padded along a unit axis,
+    # the padded copy and so the result are C-contiguous as in NumPy
+    x = rnd.random(shape)
+    if func == "irfft":
+        x = x + 1j * rnd.random(shape)
+    expected = getattr(np.fft, func)(x, n=8, axis=axis)
+
+    result = getattr(mkl_fft, func)(x, n=8, axis=axis)
+    assert result.flags.c_contiguous
+    assert_allclose(result, expected, atol=1e-12)
+
+    out = np.full_like(expected, -1, order="C")
+    result = getattr(mkl_fft, func)(x, n=8, axis=axis, out=out)
+    assert result is out
+    assert_allclose(result, expected, atol=1e-12)
+
+
 @pytest.mark.parametrize("step", [2, -1, -3])
 @pytest.mark.parametrize("func", ["fft", "ifft"])
 def test_fft_real_input_out_strided_1d(func, step):