To quickly and accurately grasp the spatial and temporal distribution of precipitation, for regional climate, hydrology and ecological environment is essential. The Yangtze River Delta is taken as the study area, extracting spectral characteristics of FY satellite imagery and performing characteristic analysis, combined with the receiving precipitation data, the characteristic set to describe the precipitation parameters was obtained and using the support vector machine superior performance of nonlinear regression put forward a kind of self-adaptive and self-learning precipitation estimation method. The results show that: satellite imagery is applied to interpretation cloud precipitation mechanism, combined with SVM, which can well express the nonlinear relationship of the Yangtze River Delta region between precipitation and cloud features. The results also demonstrate that correlation coefficient between values of modeling estimation data and receiving precipitation data is 0.85. It is shown that this method can estimate the precipitation in the region to play a role.