Uncertainty-aware precipitation generation for Earth system models with diffusion models

Earth system models (ESMs) play a vital role in understanding and forecasting the dynamics of the Earth's climate system. Accurate simulation of precipitation is especially critical for evaluating the impacts of anthropogenic climate change, anticipating extreme weather events, and devising sustainable strategies to manage water resources and mitigate related risks. However, ESMs often exhibit significant biases in precipitation simulation due to the wide range of scales involved in these processes and the substantial uncertainties they encompass. Moreover, due to computational constraints, ESM simulations still have low horizontal resolution compared to the scales relevant for precipitation.
    In this work, we present a novel framework to improve the representation of precipitation in ESMs by integrating physically modeled circulation variables with state-of-the-art generative diffusion models. Based on large-scale (1 degree) circulation fields, our method produces accurate high-resolution (0.25 degree) precipitation estimates at global scale. Our approach introduces stochasticity into the precipitation field, significantly improving the representation of extreme events and fine-scale variability while maintaining the fidelity of large-scale patterns. Our proposed methods thus provides an alternative to traditional column-based parameterization, avoiding the need for a posteriori bias correction and downscaling.
    Preliminary results highlight the ability of our generative model to produce precipitation fields with substantially smaller biases compared to those derived from classical parameterizations of the GFDL model, while achieving higher spatial resolution. In future climate scenarios, precipitation derived from parameterizations often becomes increasingly uncertain, whereas circulation variables, being more directly tied to large-scale dynamics, may provide a more stable foundation for generating high-resolution precipitation fields. Building on this, we demonstrate the application of our framework to generate daily high-resolution precipitation maps for future climate projections, offering an improved and robust tool to address critical challenges in climate impact studies.