Monthly precipitation forecast based on sunspots

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.