In order to implement the localized application of ECMWF (European Centre for Medium-Range Weather Forecasting) model well and improve the accuracy of precipitation forecast in Sichuan Province, the systematic deviation characteristics of forecast of precipitation with various magnitudes from ECMWF model were analyzed from July to September during 2020-2021. The result shows that the rain days forecasted by ECMWF model are more than the observations in Sichuan Province from July to September during 2020-2021, especially in Panxi region and western Sichuan Plateau. The heavy rain days forecasted by the model are more than the observations in southwestern Basin and Panxi region, while they are less than the observations in southern Basin. Then, the correction experiment about 24-hour cumulative precipitation forecast was carried out based on quantile mapping method, and it was applied to heavy rainfall forecast. After the correction using quantile mapping method, the TS (Threat Score) of forecast of rainstorm and above is improved by 7%-15%, and the TS of forecast of precipitation with various magnitudes is 2%-4% higher than the multi-model integrated objective forecast products. The POD (Probability of Detection) of forecast of heavy rain, rainstorm and above is improved by 10%-20%. The corrected location of rain belt in particular rainstorm areas is closer to the actual.

Based on the daily 2 m maximum and minimum temperature data from 1990 to 2019 in Sichuan Province, the temperature transitional weather processes have been analyzed statistically. Then a correction model of temperature change during transitional processes of temperature has been performed by using of NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research) daily reanalysis data and the LightGBM (Light Gradient Boosting Machine) algorithm.The results show that the area with the most temperature transitional processes is the slope transition zone between the plateau and the basin, while the least is in the basin. The number of temperature transitional processes in each region has an obviously seasonal differences with the most in spring and the least in winter, and the temperature transitional processes in spring is significantly more than those in the other three seasons. For the training set from 1990 to 2019，the LightGBM model has good performances with an overall accuracy of 78.64% and a mean absolute error of 1.35 ℃. For the independent testing set in 2020，the LightGBM model has an overall accuracy of 53.60% and a mean absolute error of 2.19 ℃, which are better than those of ECMWF (European Centre for Medium-Range Weather Forecasting), SCMOC and SPCO models.