A Generative-driven Model for Precipitation Downscaling Over Himalayan Region

Generating high-resolution (HR) weather and climate information at ~10 km or finer across the Himalayan regions remains a major challenge due to extremely high computational cost of forecasting models and complexity of atmospheric processes. Most operational global weather prediction systems run at low-resolution (LR) of ~25 km or coarser, that are inadequate for impact-based analyses of highly localized extreme weather events common to these regions. To bridge this gap, downscaling is essential for producing climate information at impact-relevant scales, with both statistical and dynamical approaches remaining widely used despite major shortcomings. The former is computationally efficient but often fail under future climate non-stationarity, while the latter, though physically consistent, is computationally expensive and constrained by domain-resolution trade-offs. Currently, there is no efficient data-driven approach that can produce regional-model-scale precipitation fields for the Himalayan region. This work presents WGAN, a deterministic deep neural generative adversarial network (GAN)-based emulator of the Weather Research and Forecasting (WRF) model for HR precipitation downscaling over the Himalayan region. The model is conditioned on LR meteorological variables from the European Centre for Medium-Range Weather Forecasts Re-Analysis version 5 (ERA5; 0.25°×0.25°) as input and is trained against HR precipitation from WRF (0.1°×0.1°), which uses ERA5 as boundary conditions. The architecture uses Wasserstein-1 distance (WGAN) in the generator and critic value functions with a gradient penalty for stable training. WGAN demonstrated the ability to generate fine-scale precipitation fields that closely matches WRF’s outputs, accurately capturing spatial patterns and the mean values. Incorporating terrain and an extreme aware-weighting MSE (Mean Squared Error) loss function in the model further improves precipitation magnitude representation, reduces biases, and yield ~29% reduction in RMSE in the upper decile. The model effectively captured low-frequency (large-scale) variability and better matches WRF’s power spectrum at mid-high frequency (short-scale) variability. This raises the probability of detection and lowers the false alarm rate across thresholds. With a case study, WGAN showed the ability to capture the fine-scale spatial distribution of precipitation in the mountains and foothills, at both extreme precipitation day and dry conditions, outperforming CNN-based precipitation output. These results underscore the capability of WGAN as a fast and efficient tool for precipitation downscaling for the Himalayan region, operating at only a fraction of the computational cost. The model has strong potential for operational use in early warning, risk assessment, vulnerability analysis, disaster management, and other sectors that rely on localized climate information, ultimately supporting the preparedness of communities living in and around these mountains.