Based on the daily ground observations in meteorological stations of Jilin Province during April-July 1997-2015,taking temperature,air pressure,relative humidity,water vapor pressure and wind speed as covariates,this paper established a statistical prediction model of daily precipitation based on self-organizing maps(SOM).This paper studied major synoptic patterns in Jilin Province and the relationship between daily precipitation and the patterns,and based on this relationship,proposed a Monte Carlo simulation method for daily precipitation.Results demonstrate that SOM has high classification quality of synoptic patterns,and the accumulative probability distributions of adjacent synoptic patterns are similar,while those of synoptic patterns far away are quite different.The correlation coefficient between the probability of no precipitation and the corresponding width of daily precipitation interval in the synoptic patterns is -0.94,and the significance level is less than 0.01.According to the accumulative probability distribution of precipitation,20 types of synoptic patterns are divided into four categories,which match the occurrence rate of precipitation and the daily precipitation.On this basis,this paper carried out Monte Carlo simulation of daily precipitation in 24 stations of Jilin Province,and analyzed the forecast performance.The median values of MAE(mean absolute error),RMSE(root mean square error),S_Brier and S_sig are 3.12 mm,6.13 mm,0.06 and 0.51,respectively,which indicates that the method has a good forecast performance in general.The distribution of MAE and RMSE is large in the southeast and small in the northwest,and all stations have smaller errors after removing the effect of the natural fluctuation of precipitation.S_Brier and S_sig have no obvious spatial distribution characteristics.