Maria Conchita Agana Navarro, Geng Li, Theo Wolf, Maria Perez-Ortiz

Researchers benchmarked three machine learning climate models, including the ClimaX foundation model, to assess their robustness when predicting climate states outside their historical training data. Testing on temporal extrapolation (2015-2023) and cross-scenario forcing shifts revealed a critical trade-off: while ClimaX achieved the lowest absolute error, it showed higher sensitivity to distribution shifts, with precipitation errors increasing up to 8.44% under extreme scenarios. The findings highlight that even high-capacity foundation models struggle with no-analog climate conditions and underscore the need for scenario-aware training and rigorous out-of-distribution evaluation protocols.