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Abstract We present a lightweight, easy‐to‐train, low‐resolution, fully data‐driven climate emulator, LUCIE, that can be trained on as low as 2 years of 6‐hourly ERA5 data. Unlike most state‐of‐the‐art AI weather models, LUCIE remains stable and physically consistent for 100 years of autoregressive simulation with 100 ensemble members. Long‐term mean climatology from LUCIE's simulation of temperature, wind, precipitation, and humidity matches that of ERA5 data, along with the variability. We further demonstrate how well extreme weather events and their return periods can be estimated from a large ensemble of long‐term simulations. We further discuss an improved training strategy with a hard‐constrained first‐order integrator to suppress autoregressive error growth, a novel spectral regularization strategy to better capture fine‐scale dynamics, and finally an optimization algorithm that enables data‐limited (as low as 2 years of 6‐hourly data) training of the emulator without losing stability and physical consistency. Finally, we provide a scaling experiment to compare the long‐term bias of LUCIE with respect to the number of training samples. Importantly, LUCIE is an easy to use model that can be trained in just 2.4 hr on a single A‐100 GPU, allowing for multiple experiments that can explore important scientific questions that could be answered with large ensembles of long‐term simulations, for example, the impact of different variables on the simulation, dynamic response to external forcing, and estimation of extreme weather events, amongst others. 

This paper describes an implementation of the Combined Hybrid-Parallel Prediction (CHyPP) approach of Wikner et al. (2020) on a low-resolution atmospheric global circulation model (AGCM). This approach combines a physics-based numerical model of a dynamical system (e.g., the atmosphere) with a computationally efficient type of machine learning (ML) called reservoir computing (RC) to construct a hybrid model. This hybrid atmospheric model produces more accurate forecasts of most atmospheric state variables than the host AGCM for the first 7-8 forecast days, and for even longer times for the temperature and humidity near the earth's surface. It also produces more accurate forecasts than a purely ML-based model, or a model that combines linear regression, rather than ML, with the AGCM. The potential of the approach for climate research is demonstrated by a 10-year long hybrid model simulation of the atmospheric general circulation, which shows that the hybrid model can simulate the general circulation with substantially smaller systematic errors and more realistic variability than the host AGCM.