biostudies-literatureBolibar JRégion Auvergne-Rhône-AlpesDutch Research Council (NWO)Fonds De La Recherche Scientifique - FNRS409https://www.ebi.ac.uk/biostudies/studies/S-EPMC8776880biostudies-literatureUnknown13(1)Glaciers and ice caps are experiencing strong mass losses worldwide, challenging water availability, hydropower generation, and ecosystems. Here, we perform the first-ever glacier evolution projections based on deep learning by modelling the 21st century glacier evolution in the French Alps. By the end of the century, we predict a glacier volume loss between 75 and 88%. Deep learning captures a nonlinear response of glaciers to air temperature and precipitation, improving the representation of extreme mass balance rates compared to linear statistical and temperature-index models. Our results confirm an over-sensitivity of temperature-index models, often used by large-scale studies, to future warming. We argue that such models can be suitable for steep mountain glaciers. However, glacier prNature communicationsNonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning.PMC8776880Postdoctoral grant- chargé de recherches016.Vidi.171.063BERGER projectBolibar JZekollari HRabatel AGouttevin IGaliez CfalseNonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning.Glaciers and ice caps are experiencing strong mass losses worldwide, challenging water availability, hydropower generation, and ecosystems. Here, we perform the first-ever glacier evolution projections based on deep learning by modelling the 21st century glacier evolution in the French Alps. By the end of the century, we predict a glacier volume loss between 75 and 88%. Deep learning captures a nonlinear response of glaciers to air temperature and precipitation, improving the representation of extreme mass balance rates compared to linear statistical and temperature-index models. Our results confirm an over-sensitivity of temperature-index models, often used by large-scale studies, to future warming. We argue that such models can be suitable for steep mountain glaciers. However, glacier pr2022-01-01T00:00:00Z2022 Jan2025-04-18T16:34:27.761Z2025-04-07T03:50:40.054ZS-EPMC87768803505846110.1038/s41467-022-28033-0