diff --git a/docs/iceberg-rest-service.md b/docs/iceberg-rest-service.md
index 5389f934f49..eb0bcc20b6b 100644
--- a/docs/iceberg-rest-service.md
+++ b/docs/iceberg-rest-service.md
@@ -120,6 +120,22 @@ For other Iceberg s3 properties not managed by Gravitino like `s3.sse.type`, you
 Please set `gravitino.iceberg-rest.warehouse` to `s3://{bucket_name}/${prefix_name}` for Jdbc catalog backend, `s3a://{bucket_name}/${prefix_name}` for Hive catalog backend.
 :::
 
+#### GCS
+
+Supports using google credential file to access GCS data.
+
+| Configuration item               | Description                                                                                        | Default value | Required | Since Version |
+|----------------------------------|----------------------------------------------------------------------------------------------------|---------------|----------|---------------|
+| `gravitino.iceberg-rest.io-impl` | The io implementation for `FileIO` in Iceberg, use `org.apache.iceberg.gcp.gcs.GCSFileIO` for GCS. | (none)        | No       | 0.7.0         |
+
+For other Iceberg GCS properties not managed by Gravitino like `gcs.project-id`, you could config it directly by `gravitino.iceberg-rest..gcs.project-id`.
+
+Please make sure the credential file is accessible by Gravitino, like using `export GOOGLE_APPLICATION_CREDENTIALS=/xx/application_default_credentials.json` before Gravitino Iceberg REST server is started.
+
+:::info
+Please set `gravitino.iceberg-rest.warehouse` to `gs://{bucket_name}/${prefix_name}`, and download [Iceberg gcp bundle](https://mvnrepository.com/artifact/org.apache.iceberg/iceberg-gcp-bundle) and place it to the classpath of Gravitino Iceberg REST server.
+:::
+
 #### HDFS configuration
 
 You should place HDFS configuration file to the classpath of the Iceberg REST server, `iceberg-rest-server/conf` for Gravitino server package, `conf` for standalone Gravitino Iceberg REST server package. When writing to HDFS, the Gravitino Iceberg REST catalog service can only operate as the specified HDFS user and doesn't support proxying to other HDFS users. See [How to access Apache Hadoop](gravitino-server-config.md#how-to-access-apache-hadoop) for more details.
@@ -284,7 +300,7 @@ For example, we can configure Spark catalog options to use Gravitino Iceberg RES
 --conf spark.sql.catalog.rest.uri=http://127.0.0.1:9001/iceberg/
 ```
 
-You may need to adjust the Iceberg Spark runtime jar file name according to the real version number in your environment. If you want to access the data stored in S3, you need to download [iceberg-aws-bundle](https://mvnrepository.com/artifact/org.apache.iceberg/iceberg-aws-bundle) jar and place it in the classpath of Spark, no extra config is needed because S3 related properties is transferred from Iceberg REST server to Iceberg REST client automaticly. 
+You may need to adjust the Iceberg Spark runtime jar file name according to the real version number in your environment. If you want to access the data stored in cloud, you need to download [iceberg aws bundle](https://mvnrepository.com/artifact/org.apache.iceberg/iceberg-aws-bundle) or [Iceberg gcp bundle](https://mvnrepository.com/artifact/org.apache.iceberg/iceberg-gcp-bundle) and place it in the classpath of Spark, no extra config is needed because S3 related properties is transferred from Iceberg REST server to Iceberg REST client automaticly. 
 
 ### Exploring Apache Iceberg with Apache Spark SQL
 
diff --git a/docs/lakehouse-iceberg-catalog.md b/docs/lakehouse-iceberg-catalog.md
index f20487fa155..2fb41c1aa0a 100644
--- a/docs/lakehouse-iceberg-catalog.md
+++ b/docs/lakehouse-iceberg-catalog.md
@@ -80,7 +80,23 @@ Supports using static access-key-id and secret-access-key to access S3 data.
 For other Iceberg s3 properties not managed by Gravitino like `s3.sse.type`, you could config it directly by `gravitino.bypass.s3.sse.type`.
 
 :::info
-Please set `gravitino.iceberg-rest.warehouse` to `s3://{bucket_name}/${prefix_name}` for JDBC catalog backend, `s3a://{bucket_name}/${prefix_name}` for Hive catalog backend.
+Please set `warehouse` to `s3://{bucket_name}/${prefix_name}` for JDBC catalog backend, `s3a://{bucket_name}/${prefix_name}` for Hive catalog backend.
+:::
+
+#### GCS
+
+Supports using google credential file to access GCS data.
+
+| Configuration item     | Description                                                                                        | Default value | Required | Since Version |
+|------------------------|----------------------------------------------------------------------------------------------------|---------------|----------|---------------|
+| `io-impl`              | The io implementation for `FileIO` in Iceberg, use `org.apache.iceberg.gcp.gcs.GCSFileIO` for GCS. | (none)        | No       | 0.7.0         |
+
+For other Iceberg GCS properties not managed by Gravitino like `gcs.project-id`, you could config it directly by `gravitino.bypass.gcs.project-id`.
+
+Please make sure the credential file is accessible by Gravitino, like using `export GOOGLE_APPLICATION_CREDENTIALS=/xx/application_default_credentials.json` before Gravitino server is started.
+
+:::info
+Please set `warehouse` to `gs://{bucket_name}/${prefix_name}`, and download [Iceberg gcp bundle jar](https://mvnrepository.com/artifact/org.apache.iceberg/iceberg-gcp-bundle) and place it to `catalogs/lakehouse-iceberg/libs/`.
 :::
 
 #### Catalog backend security
diff --git a/docs/spark-connector/spark-catalog-iceberg.md b/docs/spark-connector/spark-catalog-iceberg.md
index 3bc6166314f..ee3e005e36b 100644
--- a/docs/spark-connector/spark-catalog-iceberg.md
+++ b/docs/spark-connector/spark-catalog-iceberg.md
@@ -138,3 +138,11 @@ Gravitino catalog property names with the prefix `spark.bypass.` are passed to S
 Iceberg catalog property `cache-enabled` is setting to `false` internally and not allowed to change.
 :::
 
+## Storage
+
+- HDFS
+- GCS
+
+### GCS
+
+No extra configuration is needed. Please make sure the credential file is accessible by Spark, like using `export GOOGLE_APPLICATION_CREDENTIALS=/xx/application_default_credentials.json`, and download [Iceberg gcp bundle](https://mvnrepository.com/artifact/org.apache.iceberg/iceberg-gcp-bundle) and place it to the classpath of Spark.
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