Probabilistic Emulation of a Global Climate Model with Spherical DYffusion Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer, Christopher S. Bretherton, Rose Yu• This paper introduces Spherical DYffusion, the first conditional generative model designed for the probabilistic emulation of a global climate model. It achieves accurate and physically consistent global climate ensemble simulations by combining the dynamics-informed diffusion framework (DYffusion) with the Spherical Fourier Neural Operator (SFNO) architecture.• The model demonstrates significant improvements in climate model emulation, achieving near gold-standard performance. It substantially reduces climate biases compared to existing baselines, with errors often closer to the reference simulation’s noise floor. For example, it reduces climate biases to within 50% of the reference model, outperforming the next best baseline (ACE) by more than 2x.• Spherical DYffusion enables stable and efficient long-term climate simulations, capable of 100-year simulations at 6-hourly timesteps with low computational overhead. It offers significant speed-ups (over 25x) and energy savings compared to the physics-based FV3GFS model it emulates.• The method is particularly effective for ensemble climate simulations, accurately reproducing climate variability consistent with the reference model and further reducing climate biases through ensemble-averaging. The paper also highlights that short-term weather performance does not necessarily translate to accurate long-term climate statistics.

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