EC-EarthFlow: Probabilistic emulation of daily transient global climate model simulations with flow matching

Abstract:We introduce EC-EarthFlow, a generative flow matching model that emulates simulations from the physical climate model EC-Earth3. The model is trained on transient simulations from EC-Earth3 (1950-2166, SSP2-4.5) to predict the day ahead temperature field from the previous days temperature as well as annual mean temperature. Predictions are made auto-regressively with rollout periods of between a month and an extended season. Using only this variable of interest, we are able to reproduce the daily variability, spatial patterns, annual cycle and long-term trend from EC-Earth3 at a substantially lower computational cost than the physical model. We demonstrate that EC-EarthFlow is stable for long inference periods, and that it can learn the physical relationships as simulated in EC-Earth3.

Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)

arXiv:2610.09715 [cs.LG]

(or arXiv:2610.09715v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.09715 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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