Articles | Volume 20, issue 3
https://doi.org/10.5194/tc-20-1841-2026
https://doi.org/10.5194/tc-20-1841-2026
Research article
 | 
30 Mar 2026
Research article |  | 30 Mar 2026

Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature

Nils Bochow, Philipp Hess, and Alexander Robinson

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Short summary
This study presents a fast, physics-guided machine-learning method that downscales coarse climate fields to fine resolution while enforcing conservation of large-scale totals. Trained on regional climate simulations and driven by Earth system model output, it handles extremes and outperforms linear interpolation, providing realistic, high-resolution forcing for ice-sheet models and improving projections of Greenland’s sea-level contribution.
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