Multi-variable Hard Physical Constraints for Climate Model Downscaling
Jose González-Abad Álex Hernández-García Paula Harder David Rolnick José Manuel Gutiérrez
Global Climate Models (GCMs) are the primary tool to simulate climate
evolution and assess the impacts of climate change. However, they often operate
at a coarse spatial resolution that limits their accuracy in reproducing
local-scale phenomena. Statistical downscaling methods leveraging deep learning
offer a solution to this problem by approximating local-scale climate fields
from coarse variables, thus enabling regional GCM projections. Typically,
climate fields of different variables of interest are downscaled independently,
resulting in violations of fundamental physical properties across
interconnected variables. This study investigates the scope of this problem
and, through an application on temperature, lays the foundation for a framework
introducing multi-variable hard constraints that guarantees physical
relationships between groups of downscaled climate variables.