diff --git a/monai/transforms/intensity/array.py b/monai/transforms/intensity/array.py
index d941f43ad7..a23c867d60 100644
--- a/monai/transforms/intensity/array.py
+++ b/monai/transforms/intensity/array.py
@@ -1128,12 +1128,12 @@ def __init__(
         self.upper = upper
         self.sharpness_factor = sharpness_factor
         self.channel_wise = channel_wise
-        if return_clipping_values:
-            self.clipping_values: list[tuple[float | None, float | None]] = []
         self.return_clipping_values = return_clipping_values
         self.dtype = dtype
 
-    def _clip(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
+    def _clip(
+        self, img: NdarrayOrTensor, clipping_values: list[tuple[float | None, float | None]] | None = None
+    ) -> NdarrayOrTensor:
         if self.sharpness_factor is not None:
             lower_percentile = percentile(img, self.lower) if self.lower is not None else None
             upper_percentile = percentile(img, self.upper) if self.upper is not None else None
@@ -1143,8 +1143,8 @@ def _clip(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
             upper_percentile = percentile(img, self.upper) if self.upper is not None else percentile(img, 100)
             img = clip(img, lower_percentile, upper_percentile)
 
-        if self.return_clipping_values:
-            self.clipping_values.append(
+        if clipping_values is not None:
+            clipping_values.append(
                 (
                     (
                         lower_percentile
@@ -1165,16 +1165,17 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
         """
         Apply the transform to `img`.
         """
+        clipping_values: list[tuple[float | None, float | None]] | None = [] if self.return_clipping_values else None
         img = convert_to_tensor(img, track_meta=get_track_meta())
         img_t = convert_to_tensor(img, track_meta=False)
         if self.channel_wise:
-            img_t = torch.stack([self._clip(img=d) for d in img_t])  # type: ignore
+            img_t = torch.stack([self._clip(img=d, clipping_values=clipping_values) for d in img_t])  # type: ignore
         else:
-            img_t = self._clip(img=img_t)
+            img_t = self._clip(img=img_t, clipping_values=clipping_values)
 
         img = convert_to_dst_type(img_t, dst=img)[0]
-        if self.return_clipping_values:
-            img.meta["clipping_values"] = self.clipping_values  # type: ignore
+        if clipping_values is not None:
+            img.meta["clipping_values"] = clipping_values  # type: ignore
 
         return img
 
diff --git a/tests/transforms/test_clip_intensity_percentiles.py b/tests/transforms/test_clip_intensity_percentiles.py
index 18ed47dbaa..12d93da47d 100644
--- a/tests/transforms/test_clip_intensity_percentiles.py
+++ b/tests/transforms/test_clip_intensity_percentiles.py
@@ -192,5 +192,29 @@ def test_channel_wise(self, p):
             assert_allclose(result[i], p(expected), type_test="tensor", rtol=1e-4, atol=0)
 
 
+class TestClipIntensityPercentilesClippingValues(unittest.TestCase):
+    def test_clipping_values_repeated_channel_wise_calls(self):
+        clipper = ClipIntensityPercentiles(lower=0, upper=100, channel_wise=True, return_clipping_values=True)
+        first = clipper(torch.tensor([[[0.0, 1.0]], [[10.0, 20.0]]]))
+        first_clipping_values = list(first.meta["clipping_values"])
+
+        second = clipper(torch.tensor([[[100.0, 200.0]], [[1000.0, 2000.0]]]))
+
+        self.assertEqual(first_clipping_values, [(0.0, 1.0), (10.0, 20.0)])
+        self.assertEqual(first.meta["clipping_values"], first_clipping_values)
+        self.assertEqual(second.meta["clipping_values"], [(100.0, 200.0), (1000.0, 2000.0)])
+
+    def test_clipping_values_repeated_non_channel_wise_calls(self):
+        clipper = ClipIntensityPercentiles(lower=0, upper=100, return_clipping_values=True)
+        first = clipper(torch.tensor([[[0.0, 1.0]]]))
+        first_clipping_values = list(first.meta["clipping_values"])
+
+        second = clipper(torch.tensor([[[100.0, 200.0]]]))
+
+        self.assertEqual(first_clipping_values, [(0.0, 1.0)])
+        self.assertEqual(first.meta["clipping_values"], first_clipping_values)
+        self.assertEqual(second.meta["clipping_values"], [(100.0, 200.0)])
+
+
 if __name__ == "__main__":
     unittest.main()