Abstract: The prediction of monthly precipitation is of great significance for water resource utilization and water conservancy project scheduling. The core work of this study includes: (1) proposing a new sunspot SSN_y index and constructing an RBF forecasting model; (2) construct a multilayer-perceptron (MLP) monthly precipitation forecast model based on the SSN_y index prediction value; (3) construct the Rain Abundance Index (RAI) to overcome the contradiction between local and global optima, and using MLP for RAI prediction. The results show that: (1) The radial basis function network (RBF) forecasting model of SSN_y index performs well in the calibration, validation, and prediction stages, with a relative error of less than 10% for 95% of the predicted values in month t+1; (2) The training and testing sets of the MLP monthly precipitation forecast model have good performance, with R² values of 0.99 for both sets; (3) The definition of RAI has good physical significance and further research value; (4) The MLP monthly RAI forecast model performs well and meets the accuracy requirements. The research on monthly precipitation forecast based on sunspots has pioneering significance.