Abstract: The terrain and landform of the Sichuan Basin are complex, which also make the forecast of heavy rainfall challenging, therefore the correction of the model precipitation forecast products is the crucial method to improve the quality of heavy rainfall forecasting. In this pa⁃ per, a total of 35 heavy rainfall processes that occurred in the Sichuan Basin from 2018 to 2019 were examined to verify the 24-hour heavy rainfall prediction of ECMWF, CMA_MESO, and SWC_WARMS by using conventional score and spatial translation methods. Correction ex⁃ periments are then conducted on these models using three methods, including optimal score, multi-model integration, and displacement cor⁃ rection. The results show that the optimal score correction method can effectively improve the intensity of precipitation prediction, while the multi-model integration correction method performs better in both the precipitation fall area and extremum prediction. On this basis, the dis⁃ placement deviation is calculated, which is used to correct the displacement of precipitation prediction according to the optimal value, and thus further improve the forecast of heavy rainfall area. Finally, the performance of the correction is evaluated by using the heavy rainfall processes from 2020 to 2021. The results show that the corrected precipitation extremum prediction is closer to the actual observations, with the precipi⁃ tation prediction score of each magnitude being obviously better than that of the single model. Compared to the optimal single model, the TS score improvement rate of the rainstorm and heavy rainstorm prediction can reach 24.3 % and 42.8%, respectively. After correction, the false alarm rate (FAR) basically remains unchanged, while the missing rate (MR) significantly decreases, suggesting good correction effects.