Improving Climate Change Data through Deep Learning Super-Resolution Downscaling of GCMs for Precise Hydrological Projections

Climate change is an essential part of sustainable development challenges in developing countries. Climate change represents one of the greatest environmental, social, and economic threats facing the world today. Accurate meteorological and hydrological projections are vital for effective climate adaptation and resource management, particularly under changing climate scenarios. However, the coarse spatial resolution of General Circulation Models (GCMs) limits their applicability for localized impact assessments. This study proposes a deep learning-based super-resolution approach combined with an advanced hydrological model to downscale and enhance the spatial resolution of three GCM datasets—GFDL-CM4, GISS-E2-1-G, and IPSL-CM6A-LR—to approximately 0.01°. The performance of the method is evaluated based on mean square error (RMSE), mean absolute error (MAE), Peak signal-to-noise ratio (PSNR), and Pearson correlation coefficient (R). This study hypothesizes to have more precise and accurate meteorological and hydrological predictions and projections under this framework. The model is conducted on historical climate data and compared with high-resolution observational datasets, showcasing its ability to capture fine-scale climatic and hydrological variability. This approach bridges the resolution gap in climate projections and provides a robust framework for better-informed decision-making in climate change adaptation and mitigation strategies.

Funding

This research was supported by Disaster-Safety Platform Technology Development Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT. (No. 2022M3D7A1090338).