The accurate prediction of precipitation is crucial for agricultural management, water resources planning, and drought monitoring. One efective approach involves using a combination of linear and nonlinear models in a hybrid system. This study focuses on enhancing the hybrid model by employing the signal decomposition method, particularly for the complex nonlinear component. The research evaluated the efectiveness of incorporating seasonal autoregressive integrated moving average (SARIMA) with empirical mode decomposition (EMD) and maximal overlap discrete wavelet transform (MODWT) methods in the hybrid model structure using monthly precipitation data from stations in Iran. The procedure involved obtaining error series from the SARIMA model, decomposing the error series into intrinsic mode functions (IMFs) using EMD, and then applying support vector regression to forecast them. The evaluation criteria showed that using EMD in the hybrid model structure enhanced its efciency by reducing signifcant error criteria and increasing residual predictive deviation. The proposed model also preserved precipitation forecasts in terms of time, with overestimated forecasts exhibiting high efciency (RPD values>2.5). Additionally, incorporating MODWT as a secondary decomposition in the fnal step of the proposed model further improved precipitation forecasting accuracy compared to the hybrid model solely incorporating EMD. The assimilation of signal decomposition methods in a hybrid model can enhance the accuracy and reliability of precipitation forecasts by revealing important error patterns.
