Generative Machine Learning for Dynamically Consistent Multivariate Downscaling of Tipping Point Simulations from Global Earth System Models

Numerical Earth system model (ESM) simulations require bias correction and downscaling to assess regional climate impacts due to their coarse resolution (50-100km) and systematic errors. Recent generative machine learning-based downscaling methods show promise in capturing small-scale spatial patterns, as well as multivariate and temporal dependencies [1,2,3]. However, making these approaches efficient and scalable to high resolutions globally remains challenging.

Here, we present a generative machine learning method for multivariate and temporally consistent downscaling of global climate fields at daily and 0.25° spatial resolution.  An autoregressive consistency model [4] is trained using Patch Diffusion [5] as an efficient probabilistic emulator of the ERA5 reanalysis and applied to downscale 8 key climate impact variables, including precipitation, temperature, wind speed, and radiation.
We downscale five 100-year simulations per ESM, including pre-industrial control,  historical, and 2K warming scenarios with and without tipping of the Atlantic meridional overturning circulation and the Amazon rainforest, from three CMIP6-class ESMs (MPI-ESM1-2-HR, HadGEM3-GC31-MM, and CESM1-CAM5).

The approach accurately reproduces small-scale variability and extremes, outperforms statistical baselines, substantially reduces biases, and preserves the large-scale response of the tipping dynamics in the ESMs.

   
[1] Mardani, M., Brenowitz, N., Cohen, Y., Pathak, J., Chen, C. Y., Liu, C. C., ... & Pritchard, M., Residual corrective diffusion modeling for km-scale atmospheric downscaling, Communications Earth & Environment, 6(1), 124, 2025. 
[2] Schmidt, J., Schmidt, L., Strnad, F. M., Ludwig, N., & Hennig, P., A generative framework for probabilistic, spatiotemporally coherent downscaling of climate simulation. npj Climate and Atmospheric Science, 8(1), 270, 2025.
[3] Hess, P., Aich, M., Pan, B., & Boers, N.,  Fast, scale-adaptive and uncertainty-aware downscaling of Earth system model fields with generative machine learning, Nature Machine Intelligence, 1-11, 2025.
[4] Wang, Z., Jiang, Y., Zheng, H., Wang, P., He, P., Wang, Z., ... & Zhou, M., Patch diffusion: Faster and more data-efficient training of diffusion models, Advances in neural information processing systems, 36, 72137-72154, 2023. 
[5] Song, Y., & Dhariwal, P., Improved techniques for training consistency models, In The Twelfth International Conference on Learning Representations, 2024.