Based on the precipitation data of 155 meteorological stations in the eastern part of Northwest China from 1961 to 2022, as well as historical datasets including global atmosphere data, sea surface temperature (SST), and sea ice data, this study integrates the temporal convolutional network (TCN) module and the convolutional block attention module (CBAM) into the long short-term memory (LSTM) deep learning algorithm. A climate intelligent prediction model, CBAM-TCN-LSTM, based on the fusion of deep learning algorithms is thereby established for summer precipitation in the eastern part of Northwest China. The predictive performance of the model is verified, and its predictive capability is compared with that of other deep learning algorithms. The results show that the intelligent prediction model based on the fusion of multiple deep learning algorithms outperforms single-algorithm deep learning models. During the independent sample validation period (2018-2022), the prediction score (PS) for summer precipitation prediction ranged from 60 to 80, with an average value reaching 73.81. The anomaly correlation coefficient (ACC) was positive for all years except 2020, with an average value of 0.14, representing a significant improvement over other models. Compared with current seven mainstream models of machine learning algorithms, the CBAM-TCN-LSTM model presents superior performance across all five evaluation metrics: PS, percentage correct (PC), ACC, mean absolute error (MAE), and root mean square error (RMSE). In addition, the CBAM-TCN-LSTM model was successfully applied in the 2023 flood season prediction operation. It accurately predicted the characteristics of below-normal summer precipitation in most areas of the eastern part of Northwest China, with a PS as high as 81. By building a precipitation prediction model that combines the TCN module and the CBAM module on the basis of the LSTM model which has strong time-series predictive capability, this study provides a scientific basis and technical support for regional precipitation prediction, and the model holds a good prospect for popularization and application.