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

This is the authors' abstract. We don't add key points for this paper.

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.