diff --git a/onnxscript/rewriter/rules/common/_cast_constant_of_shape.py b/onnxscript/rewriter/rules/common/_cast_constant_of_shape.py
index 8c63d3e777..7cae5cd2c5 100644
--- a/onnxscript/rewriter/rules/common/_cast_constant_of_shape.py
+++ b/onnxscript/rewriter/rules/common/_cast_constant_of_shape.py
@@ -4,6 +4,7 @@
 
 import logging
 
+import numpy as np
 import onnx_ir as ir
 
 from onnxscript.rewriter._rewrite_rule import RewriteRule, RewriteRuleSet
@@ -17,9 +18,13 @@ def cast_constant_of_shape(op, shape, scalar, dtype):
 
 
 def fused_cast_constant_of_shape(op, shape: ir.Value, scalar: ir.Attr, dtype: ir.Attr, **_):
-    # Cast scalar (a TensorProto attribute) to the specified dtype
-    scalar_value = scalar.value.numpy().item()
-    cast_value = ir.tensor([scalar_value], dtype=ir.DataType(dtype.as_int()))
+    # Cast scalar (a TensorProto attribute) to the specified dtype. Convert the array, not a
+    # Python scalar, so that values outside the target range wrap like the Cast op instead of
+    # raising OverflowError.
+    to_dtype = ir.DataType(dtype.as_int())
+    with np.errstate(invalid="ignore"):
+        cast_array = scalar.value.numpy().astype(to_dtype.numpy()).reshape(1)
+    cast_value = ir.tensor(cast_array, dtype=to_dtype)
     return op.ConstantOfShape(shape, value=cast_value)
 
 
diff --git a/onnxscript/rewriter/rules/common/_cast_constant_of_shape_test.py b/onnxscript/rewriter/rules/common/_cast_constant_of_shape_test.py
index 9001f95806..bccba0a4ad 100644
--- a/onnxscript/rewriter/rules/common/_cast_constant_of_shape_test.py
+++ b/onnxscript/rewriter/rules/common/_cast_constant_of_shape_test.py
@@ -2,9 +2,11 @@
 # Licensed under the MIT License.
 import unittest
 
+import numpy as np
 import onnx.checker
 import onnx.parser
 import onnx_ir as ir
+import parameterized
 
 from onnxscript.rewriter.rules.common import _cast_constant_of_shape
 
@@ -49,6 +51,34 @@ def test_cast_after_constant_of_shape_without_value_is_fused(self):
         output_model_proto = ir.serde.serialize_model(model)
         onnx.checker.check_model(output_model_proto, True)
 
+    @parameterized.parameterized.expand(
+        [
+            ("int_above_uint8_range", "int32", "{300}", 2, np.array([300], np.int32)),
+            ("negative_int_to_uint8", "int32", "{-1}", 2, np.array([-1], np.int32)),
+            ("int64_to_int8", "int64", "{200}", 3, np.array([200], np.int64)),
+        ]
+    )
+    def test_fused_value_matches_cast_of_out_of_range_constant(
+        self, _, value_type, value, to, value_array
+    ):
+        model_proto = onnx.parser.parse_model(
+            f"""
+            <ir_version: 7, opset_import: [ "" : 17]>
+            agraph (int64[2] input_x) => (output)
+            {{
+                constant = ConstantOfShape <value: tensor = {value_type}[1] {value}>(input_x)
+                output = Cast <to = {to}> (constant)
+            }}
+            """
+        )
+        model = ir.serde.deserialize_model(model_proto)
+        count = _cast_constant_of_shape.rules.apply_to_model(model)
+        self.assertEqual(count, 1)
+        fused_value = model.graph[0].attributes["value"].value
+        self.assertEqual(fused_value.dtype, to)
+        expected = value_array.astype(ir.DataType(to).numpy())
+        np.testing.assert_array_equal(fused_value.numpy(), expected)
+
 
 if __name__ == "__main__":
     unittest.main()