Benchmarking Regional Thermodynamic Trends in an AI Emulator, ACE2, and a Hybrid Model, NeuralGCM

Abstract AI models have emerged as potential complements to physics‐based models, but their skill in capturing observed regional trends with important societal impacts remains unexplored. Here, we benchmark satellite‐era regional thermodynamic trends, including extremes, in an AI emulator (ACE2) and a hybrid model (NeuralGCM), against physics‐based models and ERA5. Both AI models capture regional temperature trends such as satellite‐era Arctic warming. ACE2 outperforms other models in capturing midlatitude vertical temperature trends. However, the AI models do not capture trends in heat extremes over the US Southwest. Furthermore, they do not capture drying trends in arid regions, but generally outperform physics‐based models. Our results show that a data‐driven AI emulator can perform comparably to, or better than, hybrid and physics‐based models in capturing regional thermodynamic trends. We also find that ACE2 learns much of the signal from .