With the gross error detection methods for deformation monitoring data of concrete dams, it is difficult to distinguish between gross errors and sudden data jumps caused by environmental changes. To address this problem, a method for identifying gross errors in dam deformation monitoring data is proposed. This method partitions measuring points using the K-means++ clustering algorithm, and employs the OPTICS clustering algorithm combined with the local outlier factor (LOF) algorithm to detect gross errors in the monitoring data. First, the K-means++ algorithm is used to partition the measurement point areas. Then, the OPTICS and LOF algorithms are used to determine the gross errors. Finally, the real gross errors are identified by comparing the occurrence time of gross errors at different measurement points in the same area. The case study results demonstrate that the method can effectively identify data jumps caused by environmental changes in the monitoring data, significantly improves the accuracy of gross error identification, and reduces the misjudgment rate of gross errors.