Deep Learning-based Bias Correction Method for Seasonal Prediction of Summer Rainfall in China

Seasonal prediction of summer rainfall in China plays a crucial role in decision-making, environmental protection, and socio-economic development, while it currently has a low prediction skill. We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China. Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2 (FGOALS-f2), we optimized the loss function of U-Net, trained with different hyperparameters, and selected the optimum model. U-Net model can extract multi-scale feature information and preserve spatial information, making it suitable for processing meteorological data. With this end-to-end model, the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction (e.g., Empirical Orthogonal Function), which could maximize the retention of spatio-temporal information of the input data. Optimization of the loss function enhances the prediction results and mitigates model overfitting. The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score. The skill has an average value of 0.679 in China (0°–63°N, 73°–133°E) and 0.691 in the region of the Chinese mainland, which significantly improves the dynamical prediction skill by 1357% and 4836%. This study suggests that the deep learning (U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.