<ul><li>Improving the representation of precipitation in Earth system models (ESMs) is critical for assessing the impacts of climate change and extreme events.</li><li>A novel approach based on generative machine learning is presented, which integrates a conditional diffusion model with a UNet architecture to generate accurate, high-resolution global daily precipitation fields.</li><li>The model provides ensemble predictions, capturing uncertainties in precipitation, and does not require manual fine-tuning.</li><li>By leveraging interactions between global prognostic variables, the approach offers a computationally efficient alternative to conventional schemes for modeling complex precipitation patterns.</li></ul>

Diffusion models for probabilistic precipitation generation from atmospheric variables

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