diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
index 5a8505dc6992f..b0fa4f889cda1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
@@ -951,10 +951,13 @@ case class KeyGroupedShuffleSpec(
       }
 
   override def createPartitioning(clustering: Seq[Expression]): Partitioning = {
-    val newExpressions: Seq[Expression] = clustering.zip(partitioning.expressions).map {
-      case (c, e: TransformExpression) => TransformExpression(
-        e.function, Seq(c), e.numBucketsOpt)
-      case (c, _) => c
+    assert(clustering.size == distribution.clustering.size,
+      "Required distributions of join legs should be the same size.")
+
+    val newExpressions = partitioning.expressions.zip(keyPositions).map {
+      case (te: TransformExpression, positionSet) =>
+        te.copy(children = te.children.map(_ => clustering(positionSet.head)))
+      case (_, positionSet) => clustering(positionSet.head)
     }
     KeyGroupedPartitioning(newExpressions,
       partitioning.numPartitions,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/connector/KeyGroupedPartitioningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/connector/KeyGroupedPartitioningSuite.scala
index 7798397d96b38..a1b1b8444719a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/connector/KeyGroupedPartitioningSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/connector/KeyGroupedPartitioningSuite.scala
@@ -2771,4 +2771,56 @@ class KeyGroupedPartitioningSuite extends DistributionAndOrderingSuiteBase {
       }
     }
   }
+
+  test("SPARK-54439: KeyGroupedPartitioning and join key size mismatch") {
+    val items_partitions = Array(identity("id"))
+    createTable(items, itemsColumns, items_partitions)
+
+    sql(s"INSERT INTO testcat.ns.$items VALUES " +
+      "(1, 'aa', 40.0, cast('2020-01-01' as timestamp)), " +
+      "(3, 'bb', 10.0, cast('2020-01-01' as timestamp)), " +
+      "(4, 'cc', 15.5, cast('2020-02-01' as timestamp))")
+
+    createTable(purchases, purchasesColumns, Array.empty)
+    sql(s"INSERT INTO testcat.ns.$purchases VALUES " +
+      "(1, 42.0, cast('2020-01-01' as timestamp)), " +
+      "(3, 19.5, cast('2020-02-01' as timestamp))")
+
+    withSQLConf(SQLConf.V2_BUCKETING_SHUFFLE_ENABLED.key -> "true") {
+      // `time` and `item_id` in the required `ClusteredDistribution` for `purchases`, but `item` is
+      // storage partitioned only by `id`
+      val df = createJoinTestDF(Seq("arrive_time" -> "time", "id" -> "item_id"))
+      val shuffles = collectShuffles(df.queryExecution.executedPlan)
+      assert(shuffles.size == 1, "only shuffle one side not report partitioning")
+
+      checkAnswer(df, Seq(Row(1, "aa", 40.0, 42.0)))
+    }
+  }
+
+  test("SPARK-54439: KeyGroupedPartitioning with transform and join key size mismatch") {
+    // Do not use `bucket()` in "one side partition" tests as its implementation in
+    // `InMemoryBaseTable` conflicts with `BucketFunction`
+    val items_partitions = Array(years("arrive_time"))
+    createTable(items, itemsColumns, items_partitions)
+
+    sql(s"INSERT INTO testcat.ns.$items VALUES " +
+      "(1, 'aa', 40.0, cast('2020-01-01' as timestamp)), " +
+      "(1, 'bb', 10.0, cast('2021-01-01' as timestamp)), " +
+      "(4, 'cc', 15.5, cast('2021-02-01' as timestamp))")
+
+    createTable(purchases, purchasesColumns, Array.empty)
+    sql(s"INSERT INTO testcat.ns.$purchases VALUES " +
+      "(1, 42.0, cast('2020-01-01' as timestamp)), " +
+      "(3, 19.5, cast('2021-02-01' as timestamp))")
+
+    withSQLConf(SQLConf.V2_BUCKETING_SHUFFLE_ENABLED.key -> "true") {
+      // `item_id` and `time` in the required `ClusteredDistribution` for `purchases`, but `item` is
+      // storage partitioned only by `year(arrive_time)`
+      val df = createJoinTestDF(Seq("id" -> "item_id", "arrive_time" -> "time"))
+      val shuffles = collectShuffles(df.queryExecution.executedPlan)
+      assert(shuffles.size == 1, "only shuffle one side not report partitioning")
+
+      checkAnswer(df, Seq(Row(1, "aa", 40.0, 42.0)))
+    }
+  }
 }