Based on affinity propagation clustering method, summer precipitation is regionalized over China. Summer precipitation in different regions serves as predicted objects, and preceding sea surface temperature and sea level pressure are selected to be predictors. By methods of image labeling algorithm, consecutive high correlation areas are extracted to determine the predictors. Least squares regression method is used to construct a model to predict summer precipitation in different regions. Different scoring methods including Ps, the anomaly sign sameness rate and anomaly correlation coefficient are used to validate the skills of prediction model in four different factor combination schemes. The results show that the model performs best when sea surface temperature and sea level pressure in winter and spring are together considered as factors. The averaged Ps score of cross validation is 81.4 from 1982 to 2009, with the anomaly sameness rate 63% and anomaly correlation coefficient 0.35. The retrospective forecast verification shows that the averaged scores from 2010 to 2014 are 77.1, 58% and 0.19, respectively. The reforecast skills are relatively stable every year, which means that the method has a good ability to predict summer precipitation in China. Moreover, it succeeds to predict the spatial characteristic of southern flood and northern drought in China in summer 2014.