AI RESEARCH
Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation
arXiv CS.LG
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ArXi:2603.22320v1 Announce Type: new While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs, their effective use remains challenging. The hurdles are diverse, ranging from limited accessibility and a lack of specialized knowledge to a general mistrust of ML methods that are perceived as insufficiently physical. Here, we.