In order to conduct the short-term climate prediction on summer precipitation in the middle and lower reaches of the Yangtze River, 74 circulation index data from National Climate Center and then global circulation index data such as the Western Pacific (WP) index, Multivariate ENSO Index(MEI), ENSO index and so on from NOAA were used to sum up 34 early spring factors affecting the summer precipitation in the middle and lower reaches of the Yangtze River and to analyze the relationship between early spring factors and summer rainfall. Furthermore, two kinds of summer rainfall decision tree prediction models are constructed for the middle and lower reaches of the Yangtze River during 1951-2013 based on the C4.5 algorithm of data mining. Five discriminant rules for "whether too much" and seven discriminant rules for "whether too little" were obtained. In whole data, about 80% history data are randomly selected as training set for the models with the training set accuracy of 94.12% and 93.88%, respectively. The remaining 20% data are used as the test set for the models with the prediction accuracy of 91.67% and 85.71% respectively. Results show that the summer rainfall prediction models in the middle and lower reaches of the Yangtze River based on C4.5 algorithm have higher prediction accuracy with reasonable and effective models construction. The models' discriminant rules are based on large data theory, and consider a wide range of related factors and factor permutation and combination. The models are processed by intelligent selection of key factors, and objective and automatic implement is good enough to provide a new idea and method for the short-term climate prediction on precipitation in flood season of the Yangtze River Basin.