diff --git a/pyiceberg/io/pyarrow.py b/pyiceberg/io/pyarrow.py
index 7710df76f8..d1484a834a 100644
--- a/pyiceberg/io/pyarrow.py
+++ b/pyiceberg/io/pyarrow.py
@@ -1492,14 +1492,18 @@ def _field_id(self, field: pa.Field) -> int:
 
 
 def _get_column_projection_values(
-    file: DataFile, projected_schema: Schema, partition_spec: Optional[PartitionSpec], file_project_field_ids: Set[int]
+    file: DataFile,
+    projected_schema: Schema,
+    table_schema: Schema,
+    partition_spec: Optional[PartitionSpec],
+    file_project_field_ids: Set[int],
 ) -> Dict[int, Any]:
     """Apply Column Projection rules to File Schema."""
     project_schema_diff = projected_schema.field_ids.difference(file_project_field_ids)
     if len(project_schema_diff) == 0 or partition_spec is None:
         return EMPTY_DICT
 
-    partition_schema = partition_spec.partition_type(projected_schema)
+    partition_schema = partition_spec.partition_type(table_schema)
     accessors = build_position_accessors(partition_schema)
 
     projected_missing_fields = {}
@@ -1517,6 +1521,7 @@ def _task_to_record_batches(
     task: FileScanTask,
     bound_row_filter: BooleanExpression,
     projected_schema: Schema,
+    table_schema: Schema,
     projected_field_ids: Set[int],
     positional_deletes: Optional[List[ChunkedArray]],
     case_sensitive: bool,
@@ -1541,7 +1546,7 @@ def _task_to_record_batches(
 
         # Apply column projection rules: https://iceberg.apache.org/spec/#column-projection
         projected_missing_fields = _get_column_projection_values(
-            task.file, projected_schema, partition_spec, file_schema.field_ids
+            task.file, projected_schema, table_schema, partition_spec, file_schema.field_ids
         )
 
         pyarrow_filter = None
@@ -1763,6 +1768,7 @@ def _record_batches_from_scan_tasks_and_deletes(
                 task,
                 self._bound_row_filter,
                 self._projected_schema,
+                self._table_metadata.schema(),
                 self._projected_field_ids,
                 deletes_per_file.get(task.file.file_path),
                 self._case_sensitive,
diff --git a/tests/io/test_pyarrow.py b/tests/io/test_pyarrow.py
index 45b9d9c901..cd3f9c8034 100644
--- a/tests/io/test_pyarrow.py
+++ b/tests/io/test_pyarrow.py
@@ -2846,6 +2846,7 @@ def test_task_to_record_batches_nanos(format_version: TableVersion, tmpdir: str)
             FileScanTask(data_file),
             bound_row_filter=AlwaysTrue(),
             projected_schema=table_schema,
+            table_schema=table_schema,
             projected_field_ids={1},
             positional_deletes=None,
             case_sensitive=True,
@@ -4590,3 +4591,72 @@ def test_orc_stripe_based_batching(tmp_path: Path) -> None:
         # Verify total rows
         total_rows = sum(batch.num_rows for batch in batches)
         assert total_rows == 10000, f"Expected 10000 total rows, got {total_rows}"
+
+
+def test_partition_column_projection_with_schema_evolution(catalog: InMemoryCatalog) -> None:
+    """Test column projection on partitioned table after schema evolution (https://github.com/apache/iceberg-python/issues/2672)."""
+    initial_schema = Schema(
+        NestedField(1, "partition_date", DateType(), required=False),
+        NestedField(2, "id", IntegerType(), required=False),
+        NestedField(3, "name", StringType(), required=False),
+        NestedField(4, "value", IntegerType(), required=False),
+    )
+
+    partition_spec = PartitionSpec(
+        PartitionField(source_id=1, field_id=1000, transform=IdentityTransform(), name="partition_date"),
+    )
+
+    catalog.create_namespace("default")
+    table = catalog.create_table(
+        "default.test_schema_evolution_projection",
+        schema=initial_schema,
+        partition_spec=partition_spec,
+    )
+
+    data_v1 = pa.Table.from_pylist(
+        [
+            {"partition_date": date(2024, 1, 1), "id": 1, "name": "Alice", "value": 100},
+            {"partition_date": date(2024, 1, 1), "id": 2, "name": "Bob", "value": 200},
+        ],
+        schema=pa.schema(
+            [
+                ("partition_date", pa.date32()),
+                ("id", pa.int32()),
+                ("name", pa.string()),
+                ("value", pa.int32()),
+            ]
+        ),
+    )
+
+    table.append(data_v1)
+
+    with table.update_schema() as update:
+        update.add_column("new_column", StringType())
+
+    table = catalog.load_table("default.test_schema_evolution_projection")
+
+    data_v2 = pa.Table.from_pylist(
+        [
+            {"partition_date": date(2024, 1, 2), "id": 3, "name": "Charlie", "value": 300, "new_column": "new1"},
+            {"partition_date": date(2024, 1, 2), "id": 4, "name": "David", "value": 400, "new_column": "new2"},
+        ],
+        schema=pa.schema(
+            [
+                ("partition_date", pa.date32()),
+                ("id", pa.int32()),
+                ("name", pa.string()),
+                ("value", pa.int32()),
+                ("new_column", pa.string()),
+            ]
+        ),
+    )
+
+    table.append(data_v2)
+
+    result = table.scan(selected_fields=("id", "name", "value", "new_column")).to_arrow()
+
+    assert set(result.schema.names) == {"id", "name", "value", "new_column"}
+    assert result.num_rows == 4
+    result_sorted = result.sort_by("name")
+    assert result_sorted["name"].to_pylist() == ["Alice", "Bob", "Charlie", "David"]
+    assert result_sorted["new_column"].to_pylist() == [None, None, "new1", "new2"]