Abstract: Summer (June–August) precipitation data from 88 meteorological stations in Guangxi spanning 1961–2022 were utilized, provided by the Guangxi Climate Center, along with 142 circulation characteristic indices and regionally sea-surface temperature (SST) indices from the National Climate Center of China. Key predictors were selected via correlation analysis. Using these factors, individual prediction models for summer precipitation in Guangxi were constructed using multiple linear regression, random forest, and Back Propagation (BP) neural networks. Subsequently, these were integrated into a variable-weight combination model by using the variance reciprocal weighting method. The model on data from 1961 to 2017 were trained and their forecasts for the period 2018–2022 were evaluated. The results show that the variable-weight combination model consistently outperformed each model in fitting and predictive accuracy. To further validate the effectiveness of the combined approach, empirical orthogonal function (EOF) analysis was applied to the summer precipitation anomaly series from 1961 to 2017 across the 88 stations. The variable-weight combination predictions were also evaluated on the time series corresponding to the first three EOF modes. The results corroborated the conclusions drawn from single-station predictions: the average comprehensive score for the trend anomaly Prediction skill (Ps) of the variable-weight combination predictions across the 88 stations during 2018–2022 reached 88.8 points, with annual Ps scores consistently exceeding 80, indicating significantly better and more stable performance compared with all three individual models. This study provides a new approach for summer precipitation prediction in Guangxi and the development of combined prediction models.