[2603.23043] Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts

Computer Science > Machine Learning arXiv:2603.23043 (cs) [Submitted on 24 Mar 2026] Title:Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts Authors:Maria Conchita Agana Navarro, Geng Li, Theo Wolf, Maria Perez-Ortiz View a PDF of the paper titled Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts, by Maria Conchita Agana Navarro and Geng Li and Theo Wolf and Maria Perez-Ortiz View PDF HTML (experimental) Abstract:The accelerating pace of climate change introduces profound non-stationarities that challenge the ability of Machine Learning based climate emulators to generalize beyond their training distributions. While these emulators offer computationally efficient alternatives to traditional Earth System Models, their reliability remains a potential bottleneck under "no-analog" future climate states, which we define here as regimes where external forcing drives the system into conditions outside the empirical range of the historical training data. A fundamental challenge in evaluating this reliability is data contamination; because many models are trained on simulations that already encompass future scenarios, true out-of-distribution (OOD) performance is often masked. To address this, we benchmark the OOD robustness of three state-of-the-art architectures: U-Net, ConvLSTM, and the ClimaX foundation model specifically restricted to a historical-only training regime (1850-2014). We evaluate these...