Today’s most widely used climate simulators often struggle: They can’t fully capture critical small-scale processes, like thunderstorms or towering tropical clouds, because of computational limits. To capture these features, scientists run ultra-high-resolution simulations called cloud-resolving models (CRMs). These simulations track how clouds form and evolve—but they’re so expensive, running one for a decade of global climate forecasts is practically impossible. What if we could distill the wisdom of these detailed simulations into a machine learning model that runs tens to hundreds of times faster?
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