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

Kirien Whan, Nikolaj T. Mücke, Karin van der Wiel
10/7/2026
cs.LGphysics.ao-ph

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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.

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Cite this paper

@article{whan2026ecearthflow,
  title  = {EC-EarthFlow: Probabilistic emulation of daily transient global climate model simulations with flow matching},
  author = {Kirien Whan and Nikolaj T. Mücke and Karin van der Wiel},
  year   = {2026},
  eprint = {2610.09715},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2610.09715v1}
}

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