In order to improve the practical prediction ability of refined grid precipitation, the performance of ECMWF （EC）, CMA-MESO, SXWRF and SCMOC precipitation forecast products in Shaanxi Province during the rainy season of 2021 are compared and evaluated, and the correction effect of Kalman dynamic frequency matching method on different models is discussed. Then, for the shortcomings of this method, based on the optimal TS scoring threshold method and SCMOC’s judgment of weather process, the small-magnitude precipitation is revised for the second time. Finally, the heavy precipitation is revised by using the precipitation sub process modeling and the improved Kalman dynamic frequency matching method which is based on image similarity recognition technology. The results show that SCMOC has the highest accuracy of sunny and rainy forecast and the highest TS score of heavy precipitation, which are 81.60% and 0.30 respectively. The Kalman dynamic frequency matching method can significantly improve the accuracy of sunny and rainy forecast of EC, CMA-MESO and SXWRF precipitation forecast products, but the improvement effect of heavy rainfall forecast is unstable. The improvement ranges of the EC model sunny and rainy forecast accuracy and the TS score of rainstorm forecast are the largest, 6.35% and 6.99% respectively. This correction method is more suitable for EC model. Compared with the EC model modified by Kalman dynamic frequency matching method, the accuracy of sunny rain and light rain prediction of EC model after the second correction of sunny rain spaced elimination is improved by 0.51% and 0.64%, respectively. The correction of the precipitation sub process modeling can further improve the TS score of EC model heavy precipitation, which is 1.05% higher than the TS score of heavy precipitation without sub process correction. Other scoring indicators of heavy precipitation are also better. The improved Kalman dynamic frequency matching method can significantly further improve the TS score of EC precipitation of all magnitudes, especially the TS score of heavy precipitation, improved by 2.79%.