diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
index 6776f88ed1ef8..91635d346605c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
@@ -6289,6 +6289,19 @@ object SQLConf {
       .booleanConf
       .createWithDefault(false)
 
+  val LEGACY_JDBC_TIME_MAPPING_ENABLED =
+    buildConf("spark.sql.legacy.jdbc.timeMapping.enabled")
+      .internal()
+      .doc("When true, JDBC TIME columns are read as TimestampType (the legacy behavior), even " +
+        "when spark.sql.timeType.enabled is true. This is an escape hatch for workloads that " +
+        "rely on the old TIME-to-timestamp mapping. When false, JDBC TIME columns are read as " +
+        "TimeType if spark.sql.timeType.enabled is true. Has no effect when " +
+        "spark.sql.timeType.enabled is false.")
+      .version("4.3.0")
+      .withBindingPolicy(ConfigBindingPolicy.SESSION)
+      .booleanConf
+      .createWithDefault(false)
+
   val PYTHON_FILTER_PUSHDOWN_ENABLED = buildConf("spark.sql.python.filterPushdown.enabled")
     .internal()
     .doc("When true, enable filter pushdown to Python datasource, at the cost of running " +
@@ -8156,6 +8169,9 @@ class SQLConf extends Serializable with Logging with SqlApiConf {
   def legacyPostgresDatetimeMappingEnabled: Boolean =
     getConf(LEGACY_POSTGRES_DATETIME_MAPPING_ENABLED)
 
+  def legacyJdbcTimeMappingEnabled: Boolean =
+    getConf(LEGACY_JDBC_TIME_MAPPING_ENABLED)
+
   override def legacyTimeParserPolicy: LegacyBehaviorPolicy.Value =
     getConf(SQLConf.LEGACY_TIME_PARSER_POLICY)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
index 1ad38e7712080..4d67061e0a0cd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
@@ -229,7 +229,7 @@ object JdbcUtils extends Logging with SQLConfHelper {
     case java.sql.Types.SQLXML => StringType
     case java.sql.Types.STRUCT => StringType
     case java.sql.Types.TIME =>
-      if (conf.isTimeTypeEnabled) {
+      if (conf.isTimeTypeEnabled && !conf.legacyJdbcTimeMappingEnabled) {
         // Use reported scale (fractional digits) as precision; TIME(0) is valid
         val timePrecision = if (scale >= 0 && scale <= TimeType.MAX_PRECISION) scale
           else TimeType.DEFAULT_PRECISION
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index 4e75e1807cf4c..f353f6f2a6997 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -768,6 +768,32 @@ class JDBCSuite extends SharedSparkSession {
     assert(rows(0).getAs[java.time.LocalTime](0) === java.time.LocalTime.of(12, 34, 56))
   }
 
+  test("SPARK-57555: legacy.jdbc.timeMapping escape hatch keeps TIME as TimestampType") {
+    // The escape hatch forces the legacy TIME-to-timestamp mapping even when the TIME type is
+    // enabled, so workloads relying on the old behavior are not silently broken.
+    withSQLConf(
+      SQLConf.TIME_TYPE_ENABLED.key -> "true",
+      SQLConf.LEGACY_JDBC_TIME_MAPPING_ENABLED.key -> "true") {
+      val df = spark.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties())
+      assert(df.schema("A").dataType === TimestampType)
+    }
+    // Sanity check: without the escape hatch the column is read as TimeType.
+    withSQLConf(
+      SQLConf.TIME_TYPE_ENABLED.key -> "true",
+      SQLConf.LEGACY_JDBC_TIME_MAPPING_ENABLED.key -> "false") {
+      val df = spark.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties())
+      assert(df.schema("A").dataType.isInstanceOf[TimeType])
+    }
+    // The escape hatch has no effect when the TIME type is disabled: the column is read as
+    // TimestampType regardless of the escape hatch.
+    withSQLConf(
+      SQLConf.TIME_TYPE_ENABLED.key -> "false",
+      SQLConf.LEGACY_JDBC_TIME_MAPPING_ENABLED.key -> "true") {
+      val df = spark.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties())
+      assert(df.schema("A").dataType === TimestampType)
+    }
+  }
+
   test("SPARK-57555: JDBC TIME write round-trip") {
     val url = urlWithUserAndPass
     val tableName = "TEST.TIME_ROUNDTRIP"