Multi-Variable Hard Physical Constraints for Climate Model Downscaling


  • Jose González-Abad Instituto de Física de Cantabria (IFCA) CSIC-UC
  • Álex Hernández-García Mila Quebec AI Institute University of Montreal
  • Paula Harder Fraunhofer ITWM University of Kaiserslautern
  • David Rolnick Mila Quebec AI Institute McGill University
  • José Manuel Gutiérrez Instituto de Física de Cantabria (IFCA) CSIC-UC



Climate Change, Deep Learning, Climate Downscaling, Hard Constraints


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.






Artificial Intelligence and Climate: The Role of AI in a Climate-Smart Sustainable Future