A novel optimized hybrid VMD-PCA-XGBoost model for forecasting precipitation: Exemplified by the Beijing-Tianjin-Hebei study region in China

Extreme precipitation events pose significant challenges to societal infrastructure and environmental stability, particularly in vulnerable regions like the Beijing-Tianjin-Hebei area of China. Traditional forecasting methods, such as numerical weather prediction, radar nowcasting, downscaling techniques, etc., frequently fail to capture the complex nonlinear dynamics of such events. In this study, we propose a novel hybrid model, variational mode decomposition-principal component analysis-extreme gradient boosting (VMD-PCA-XGBoost), which integrates VMD for effective signal processing, PCA for dimensionality reduction, and XGBoost for enhancing predictive accuracy. Utilizing the 2023 data from 13 global navigation satellite system and meteorological stations, our model, when rigorously compared with those of XGBoost and empirical mode decomposition-based models, achieves superior performance, with average critical success index, probability of detection, and false alarm rate of 52.14%, 73.07%, and 35.98%, respectively. These findings underscore the model’s robustness and precision, offering a promising tool for improving precipitation forecasts. This study not only advances the methodological framework for atmospheric forecasting but also provides critical insights for enhancing disaster preparedness and mitigation strategies in climate-sensitive regions.