Abstract: Based on ERA5 data from 1979 to 2024,monthly precipitation data for the African continent from 1979 to 2019 and daily precipitation data from 2020 to 2024 are selected as indicators.Wavelet transform and other methods are employed to investigate the periodic characteristics of precipitation across different African regions.A hybrid wavelet-recurrent neural network(RNN) forecasting model is developed and compared with standalone RNN models.The results reveal that African precipitation exhibits significant non-stationarity and multi-scale periodicity,including seasonal cycles of 12－15 months,short-term cycles of 1－4 months,and long-term cycles of 96－128 months,reflecting the influences of monsoons and climate variability.The prediction accuracies of standalone RNN models(RNN,GRU,LSTM) are 0.522,0.516,and 0.515,respectively.In contrast,the hybrid wavelet-RNN model significantly improves forecasting accuracy,reducing MAE by approximately 60%,RMSE by approximately 66%,and increasing R² by approximately 70%.The MAE values reaches 0.000 339,0.000 338,and 0.000 337,while RMSE values reaches 0.000 626,0.000 628,and 0.000 622,and R² values reaches 0.889,0.886,and 0.891,respectively.The LSTM-WT model performs best in long-term trend prediction(R²≈0.88) and demonstrates enhanced capability in predicting 4～6 mm precipitation events.This study provides a scientific basis for water resource management,agricultural planning,and sustainable development in Africa.

Key words: Wavelet transform recurrent, Neural network, African precipitation, Spatiotemporal characteristics, Prediction model