diff --git a/dataframely/columns/decimal.py b/dataframely/columns/decimal.py
index 9a072ea..7d5d719 100644
--- a/dataframely/columns/decimal.py
+++ b/dataframely/columns/decimal.py
@@ -98,7 +98,11 @@ def dtype(self) -> pl.DataType:
         return pl.Decimal(self.precision, self.scale)
 
     def validate_dtype(self, dtype: PolarsDataType) -> bool:
-        return dtype.is_decimal()
+        return (
+            isinstance(dtype, pl.Decimal)
+            and dtype.scale == self.scale
+            and (self.precision is None or dtype.precision == self.precision)
+        )
 
     def sqlalchemy_dtype(self, dialect: sa.Dialect) -> sa_TypeEngine:
         if self.scale and not self.precision:
diff --git a/tests/column_types/test_decimal.py b/tests/column_types/test_decimal.py
index 11719e4..ec4dc03 100644
--- a/tests/column_types/test_decimal.py
+++ b/tests/column_types/test_decimal.py
@@ -66,7 +66,7 @@ def test_invalid_args(kwargs: dict[str, Any]) -> None:
 
 
 @pytest.mark.parametrize(
-    "dtype", [pl.Decimal, pl.Decimal(12), pl.Decimal(None, 8), pl.Decimal(6, 2)]
+    "dtype", [pl.Decimal, pl.Decimal(12), pl.Decimal(None, 0), pl.Decimal(6, 0)]
 )
 def test_any_decimal_dtype_passes(dtype: DataTypeClass) -> None:
     df = pl.DataFrame(schema={"a": dtype})
@@ -171,3 +171,58 @@ def test_validate_range(
     actual = evaluate_rules(lf, rules_from_exprs(column.validation_rules(pl.col("a"))))
     expected = pl.LazyFrame(valid)
     assert_frame_equal(actual, expected)
+
+
+@pytest.mark.parametrize(
+    ("schema_precision", "schema_scale", "dtype", "should_pass"),
+    [
+        # Exact match should pass
+        (38, 10, pl.Decimal(38, 10), True),
+        # Wrong scale should fail
+        (38, 10, pl.Decimal(38, 3), False),
+        # Wrong precision should fail
+        (10, 2, pl.Decimal(38, 2), False),
+        # Both wrong should fail
+        (10, 2, pl.Decimal(38, 5), False),
+        # precision=None should accept any precision with matching scale
+        (None, 5, pl.Decimal(10, 5), True),
+        (None, 5, pl.Decimal(20, 5), True),
+        # precision=None with wrong scale should fail
+        (None, 5, pl.Decimal(10, 3), False),
+    ],
+)
+def test_precision_scale_validation(
+    schema_precision: int | None,
+    schema_scale: int,
+    dtype: DataTypeClass,
+    should_pass: bool,
+) -> None:
+    class TestSchema(dy.Schema):
+        a = dy.Decimal(precision=schema_precision, scale=schema_scale)
+
+    df = pl.DataFrame(schema={"a": dtype})
+    assert TestSchema.is_valid(df) == should_pass
+
+
+@pytest.mark.parametrize(
+    ("schema_precision", "schema_scale", "input_dtype"),
+    [
+        (38, 10, pl.Decimal(38, 3)),
+        (10, 2, pl.Decimal(38, 2)),
+        (10, 5, pl.Decimal(20, 3)),
+    ],
+)
+def test_precision_scale_casting(
+    schema_precision: int,
+    schema_scale: int,
+    input_dtype: pl.DataType,
+) -> None:
+    class TestSchema(dy.Schema):
+        a = dy.Decimal(precision=schema_precision, scale=schema_scale)
+
+    df_input = pl.DataFrame({"a": [decimal.Decimal("12.34")]}).with_columns(
+        pl.col("a").cast(input_dtype)
+    )
+    df_validated = TestSchema.validate(df_input, cast=True)
+    assert df_validated.schema["a"].precision == schema_precision  # type: ignore[attr-defined]
+    assert df_validated.schema["a"].scale == schema_scale  # type: ignore[attr-defined]