PSRGAN: Generative Adversarial Networks for Precipitation Downscaling

This study proposes an innovative generative adversarial network (GAN)-based downscaling model for precipitation, named PSRGAN, which aims to enhance the spatial resolution of meteorological data using deep learning techniques. The PSRGAN model integrates a multi-scale feature fusion module (Rception), an attention mechanism (KAM), and the generator-discriminator framework of GANs to address challenges such as data sparsity and spatiotemporal correlations that traditional precipitation super-resolution methods struggle with. By extracting multi-scale spatial features, PSRGAN improves the model’s ability to detect key precipitation regions and enhances the accuracy of predicting extreme precipitation events.The model is trained and tested using low- and high-resolution simulated datasets based on regional climate models, with performance evaluated through various metrics. The experimental results demonstrate that PSRGAN achieves strong performance in the precipitation downscaling task.