A monthly early rainy season precipitation prediction model is devised by means of the partial least squares regression (PLS) method. The input factors of the model are selected from a large quantity of preceding period high correlation factors by using the empirical orthogonal function (EOF) method. The model converts the prediction of multi site monthly precipitation to that of the principal component of that field. According to the approximate invariability of eigenvectors of climate fields, the return computation is conducted to get the monthly precipitation forecasts of more than one site, together with the principal component predicted by the PLS model. A 6 year independent sample test is carried out on the monthly early rainy season precipitation prediction for 37 basic stations in Guangxi. The results show that the model has good forecasting ability.