Improving the Seasonal Forecast of Summer Precipitation in Southeastern China Using a CycleGAN-based Deep Learning Bias Correction Method

Accurate seasonal precipitation forecasts, especially for extreme events, are crucial to preventing meteorological hazards and their potential impacts on national development, social activity, and security. However, the intensity of summer precipitation is often largely underestimated in many current dynamic models. This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks (CycleGAN) to improve the seasonal forecasts for June-July-August precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System (NUIST-CFS 1.0). The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping (QM) method. Using the unpaired bias-correction model, we can also obtain advanced forecasts of the frequency, intensity, and duration of extreme precipitation events over the dynamic model predictions. This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.