Potsdam Institute for Climate Impact Research, Potsdam, Germany
Munich Climate Center and Earth System Modelling Group, Department of Aerospace and Geodesy, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
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Total article views: 6,810 (including HTML, PDF, and XML)
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6,662
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27
6,810
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PDF: 121
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Total: 6,810
BibTeX: 42
EndNote: 38
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Total article views: 979 (including HTML, PDF, and XML)
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839
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979
42
38
HTML: 839
PDF: 121
XML: 19
Total: 979
BibTeX: 42
EndNote: 38
Views and downloads (calculated since 30 Mar 2026)
Cumulative views and downloads
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Total article views: 5,831 (including HTML, PDF, and XML)
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5,823
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8
5,831
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Total: 5,831
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 28 Aug 2025)
Cumulative views and downloads
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Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 6,810 (including HTML, PDF, and XML)
Thereof 6,810 with geography defined
and 0 with unknown origin.
Total article views: 979 (including HTML, PDF, and XML)
Thereof 979 with geography defined
and 0 with unknown origin.
Total article views: 5,831 (including HTML, PDF, and XML)
Thereof 5,831 with geography defined
and 0 with unknown origin.
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.
This study presents a fast, physics-guided machine-learning method that downscales coarse...