In order to improve the prediction precision of monthly precipitation, the precipitation prediction model of WD-COA-LSTM is proposed based on wavelet decomposition (WD), coyote optimization algorithm (COA) and long short-term memory (LSTM) neural network. Firstly, the time series is preprocessed by WD to eliminate its non-stationarity, and a low-frequency sequence and three high-frequency sequences are obtained as the result. Then the parameters of the LSTM model are optimized by COA. Finally, the predicted monthly precipitation is obtained by superimposing the predicted values of each subsequence. The proposed model was applied to the monthly precipitation prediction of Baitu Town in Luanchuan County and Guxian Town in Luoning County, Luoyang City, and the results were then compared with those of the LSTM, COA-LSTM and WD-LSTM models. It is found that the proposed WD-COA-LSTM model produced the highest prediction accuracy, indicating that WD and COA can improve the precision and generalization ability of LSTM model. This model provides a new approach for the prediction of monthly precipitation.