Actual rainfall forecast is critical to the management and allocation of water resources. In recent years, deep learning has been proved to be superior to traditional forecasting methods when predicting rainfall time series with high temporal and spatial variability. In this study, the discrete wavelet transform (DWT) and two typical deep learning approaches, namely long-short term memory (LSTM) and dilated causal convolutional neural network (DCCNN), are integrated innovatively and the hybrid model (DWT-CLSTM-DCC…