Bias correction and downscaling of precipitation simulations from Earth system models with generative machine learning

Accurately projecting precipitation changes under anthropogenic global warming is crucial due to the high ecological and socio-economic impacts, especially of extreme events. Earth system model (ESM) simulations that numerically solve the governing equations on a discretized grid are our primary tool for projecting the impacts of changing precipitation characteristics in a warming climate. However, the limited resolution and complexity of current ESMs can introduce systematic errors in the numerical simulations, such as an underestimation of extremes and reduced spatial variability.
Recently, generative machine learning methods have been applied to bias-correct precipitation fields [1,2]. While demonstrating comparable or better results than established statistical approaches, these methods can suffer from training instabilities and require computationally costly retraining for each Earth system model individually. Moreover, they only allow for limited control over the spatial scale at which biases are corrected.
Here, we propose a new approach that promises to address the above issues and can correct different ESMs at a chosen spatial scale. We apply our method to bias-correct and downscale global precipitation simulations from the POEM ESM with three degrees spatial resolution. 
Different approaches to control the spatial consistency between the downscaled fields and the ESM are evaluated, such as noised initial conditions [3] and stabilization constraints [4].
     
References
    
[1] Hess, P., Drüke, M., Petri, S., Strnad, F. M., & Boers, N. (2022). Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. Nature Machine Intelligence, 4(10), 828-839.
    
[2] Harris, L., McRae, A. T., Chantry, M., Dueben, P. D., & 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.

[3] Bischoff, T., & Deck, K. (2023). Unpaired Downscaling of Fluid Flows with Diffusion Bridges. arXiv preprint arXiv:2305.01822.

[4] White, A., Kilbertus, N., Gelbrecht, M., & Boers, N. (2023). Stabilized Neural Differential Equations for Learning Constrained Dynamics. arXiv preprint arXiv:2306.09739.