AI RESEARCH
No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
arXiv CS.LG
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ArXi:2605.22248v1 Announce Type: new Climate emulation is an out-of-distribution (OOD) projection task. This is precisely the challenge where modern Machine Learning (ML) methods are most prone to failure. Consequently, while current ML emulators trained on present climate achieve high in-distribution performance, their future reliability under the inevitable distribution shifts of a changing climate remains a critical, poorly understood blind spot. Addressing this challenge requires a fundamental shift in how we understand, evaluate, and design climate emulators.