diff --git a/backends/arm/_passes/decompose_avg_pool2d_pass.py b/backends/arm/_passes/decompose_avg_pool2d_pass.py
index e0c974598bc..f19c4fb515c 100644
--- a/backends/arm/_passes/decompose_avg_pool2d_pass.py
+++ b/backends/arm/_passes/decompose_avg_pool2d_pass.py
@@ -46,7 +46,12 @@ def _compute_post_pad(
     divisor_override,
 ) -> int | torch.SymInt:
 
-    if pad == 0:
+    # Without padding, count_include_pad=True divides a window that runs past
+    # the end of the input by its valid elements only, as the decomposed
+    # count_include_pad=False avg_pool2d does. A divisor_override needs full
+    # windows though, so the part of a ceil_mode window outside the input must
+    # still be padded with zeros.
+    if pad == 0 and not (ceil_mode and divisor_override is not None):
         return pad
     pad_adjust = adjust_pooling_pad_if_needed(size, kernel, stride, pad, ceil_mode)
 
@@ -141,7 +146,7 @@ def call_operator(self, op, args, kwargs, meta):
             divisor_override,
         )
 
-        if count_include_pad and (pad_h > 0 or pad_w > 0):
+        if count_include_pad and any(p != 0 for p in pad):
             if op in aten_avg_pool2d:
                 pad_args = (x, pad, "constant", 0.0)
             else:
diff --git a/backends/arm/test/ops/test_avg_pool2d.py b/backends/arm/test/ops/test_avg_pool2d.py
index 9d2e863a31e..7ec9e3cb77d 100644
--- a/backends/arm/test/ops/test_avg_pool2d.py
+++ b/backends/arm/test/ops/test_avg_pool2d.py
@@ -110,6 +110,14 @@ def forward(self, x: torch.Tensor):
         AvgPool2d(3, 2, 1, True, True, divisor_override=2),
         (torch.rand(1, 1, 14, 14),),
     ),
+    "divisor_override_ceil_mode_no_padding": lambda: (
+        AvgPool2d(3, 2, 0, True, False, divisor_override=2),
+        (torch.rand(1, 1, 15, 14),),
+    ),
+    "divisor_override_ceil_mode_no_padding_count_include_pad": lambda: (
+        AvgPool2d(3, 2, 0, True, True, divisor_override=4),
+        (torch.rand(1, 1, 14, 14),),
+    ),
     "non_divisible_no_padding": lambda: (
         AvgPool2d(3, 2, 0),
         (torch.rand(1, 16, 56, 56),),