Statistics show that there was more precipitation in the first rainy season (April, May, June) , but the second rainy seasonal (July, August, September) precipitation is usually not obvious, and the yearly variation of precipitation is obvious unimodal distribution, base on the monthly precipitation data of 18 stations in Guangdong Beijiang river basin from 1965 to 2007. The precipitation in April and May were mainly down-ward trend, but mainly upward trend in June from 1965 to 2007 by Mann-Kendall test. Based on the significance of early sea surface temperature (SST) and teleconnection indices to the prediction of precipitation, the paper analyses the correlation between the PC1 of EOF in the first rainy seasonal precipitation and the same period and early one to twelve months globe SST and 10 teleconnection indices. The results showed that SST anomaly key areas are not only distributed in Chinas neighboring seas; SST anomaly key areas and teleconnection indices and its key periods are larger differences among months in first rainy season. SST anomaly key area of all months showed that sea areas of negative correlation are bigger than the positive correlation. The positive correlational sea areas of April and May are mainly distributed in the southern hemisphere, while June mainly distributed in the North Pacific Ocean. The negative correlational sea areas of April mainly distributed in northern hemisphere; and the negative correlational sea areas of May mainly distributed in the East Atlantic-the Mediterranean Sea, northwest Pacific Ocean, South Americas southern waters, Australias western and southern waters; while June mainly distributed in Australia-centered sea areas. Some sea areas are significantly related to the first rainy seasonal precipitation at the same time and lag one to twelve months. The statistics showed that precipitation in the first rainy season correlated significantly with PNA, AAOI and NAO; and PNA was positive correlation, AAOI was negative correlation. Most of the teleconnection indices that correlated significantly with precipitation in May and June were negative correlation. This study will help to reduce the uncertainty of the first rainy seasonal precipitation forecasts in Guangdong Beijiang River Basin, and to provide a scientific basis for the guidance of industrial and agricultural production and the disaster prevention and mitigation, but also to provide a way for the study of regional climate change and its influencing factors.