Abstract: To address the issue of low accuracy in existing monthly precipitation prediction methods, an attention mechanism and LSTM-CCN for the monthly precipitation prediction method were proposed.Firstly, the long short-term memory neural network(LSTM) was used to extract the temporal feature distribution of meteorological data, capturing the statistical distribution in adjacent or long-distance meteorological data segments from a temporal correlation perspective.Secondly, the causal convolutional network(CCN) projected meteorological data to the spatial dimension, deeply capturing the statistical distribution of spatial features of meteorological data.Thirdly, the time and space features were input into the cross-attention network in parallel, constructing a fused spatiotemporal feature.Finally, a decoder constructed with the long short-term memory neural network took the fused spatiotemporal feature as input, and the predicted monthly precipitation served as the output.The test was carried out on the data set from Xinxiang City, Henan Province from 2001 to 2017.The results showed that the proposed method′s root mean square error was only 13.08 mm, demonstrating lower prediction errors compared to mainstream methods.The introduction of this work contributes to enhancing the accuracy and practicality of meteorological predictions.