Abstract: The Rotated Empirical Orthogonal Functions (REOF) and correlation analysis are used to get the summer precipitation forecasting objects, the vegetation and ocean factors over the Yangtze River Basin, based on the 1982-2006 monthly Normalized Difference Vegetation Index (NDVI) from the Global Inventory Monitoring and Modeling Studies (GIMMS) in USA, the 1854-2008 sea surface temperature (SST) data supported by the American National Oceanic and Atmospheric Administration (NOAA) and the 1951-2006 monthly precipitation dataset of 160 stations from the National Climate Center (NCC) of China. The optimal subset regression models (OSR), the cross validation (CV) tests and space reconstruction methods, are respectively introduced to analyze the improvement owing to the incorporation of the vegetation factors, the spring vegetation impacts on the summer precipitation predictability and robustness over the Yangtze River Basin, under the two different situations of ocean factors alone and both ocean and land vegetation included. The results show that: (1) The spring land vegetation is at least as important as the ocean temperature indices. Compared with the pure pre-spring SST forecast models, the predictability of the Yangtze River Basin is obviously improved after the introduction of the pre-spring vegetation factors. The average forecast correlations are increased by about 0.17 from 0.49 to 0.66 (the explained variance is increased by about 60%), especially for the poor prediction areas using SST factors alone such as the Han River-Huai River subarea and the Huai River Basin subarea in which the correlations are raised by about 0.20-0.30 (the explained variance is increased by about 100%). (2) The cross validation results indicate that the pure SST forecast models of the Yangtze River Basin have the low prediction robustness in which the cross validation forecast correlations have the great drop. The introduction of the vegetation NDVI factors can receive the better performance in which the forecast correlations are significantly improved over the middle-lower reaches of the Yangtze River Basin and its south such as the Yangtze River Delta subarea and the Dongting Lake-Poyang Lake subarea. And, (3) there are the obvious predictability difference among the subareas over the Yangtze River Basin, which show the necessity of the subareas forecasts one by one. The best performance areas are the Jialing River Basin subarea and the Han River-Dongting Lake subarea, and the worst ones are the Han River-Huai River subarea, the Huai River Basin subarea and the Yangtze River Delta subarea. In spite of the well simulation results, the Dongting Lake-Poyang Lake subarea shows the weaker robustness for the cross validation tests in which the forecast correlation coefficients are reduced by 0.27.