Downscaling precipitation simulations from Earth system models with generative machine learning

Assessing precipitation impacts due to anthropogenic climate change relies on accurate and high-resolution numerical Earth system model (ESM) simulations. However, such simulations are computationally too expensive, and their discretized formulation can introduce systematic errors. These can, for example, lead to an underestimation of spatial intermittency and extreme events.
Generative machine learning has been shown to skillfully downscale and correct precipitation fields from numerical simulations [1].
However, these approaches require separate training for each Earth system model, making corrections of large ESM ensembles computationally costly.
Here, we follow a diffusion-based approach [2] by training an unconditional generative consistency model [3] on high-resolution ERA5 precipitation data. Once trained, a single generative model can be used to efficiently downscale arbitrary ESM simulations in an uncertainty-aware and scale-adaptive manner. Using three different climate models, GFDL-ESM4 [4], POEM [5], and SpeedyWeather [6], we evaluate the performance and generalizability of our approach.

[1] Harris, L., McRae, A.T., Chantry, M., Dueben, P.D. and Palmer, T.N., 2022. A generative deep learning approach to stochastic downscaling of precipitation forecasts. Journal of Advances in Modeling Earth Systems, 14(10), e2022MS003120.
[2] Hess, P., Aich, M., Pan, B., and Boers, N., 2024. Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Machine Learning. arXiv preprint arXiv:2403.02774.
[3] Song, Y., Dhariwal, P., Chen, M., and Sutskever, I. 2023.  Consistency Models. In International Conference on Machine Learning (pp. 32211-32252).
[4] Dunne, J.P., Horowitz, L.W., Adcroft, A.J., Ginoux, P., Held, I.M., John, J.G., Krasting, J.P., Malyshev, S., Naik, V., Paulot, F. and Shevliakova, E., 2020. The GFDL Earth System Model version 4.1 (GFDL‐ESM 4.1): Overall coupled model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12(11), e2019MS002015.
[5] Drüke, M., von Bloh, W., Petri, S., Sakschewski, B., Schaphoff, S., Forkel, M., Huiskamp, W., Feulner, G. and Thonicke, K., 2021. CM2Mc-LPJmL v1.0: biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model. Geoscientific Model Development 14, 4117–4141.
[6] Klöwer, M., Gelbrecht, M., Hotta, D., Willmert, J., Silvestri, S., Wagner, G.L., White, A., Hatfield, S., Kimpson, T., Constantinou, N.C. and Hill, C., 2024. SpeedyWeather.jl: Reinventing atmospheric general circulation models towards interactivity and extensibility. Journal of Open Source Software, 9(98), 6323.