  Data-driven deep learning models are on the verge of transforming global
weather forecasting. It is an open question if this success can extend to
climate modeling, where long inference rollouts and data complexity pose
significant challenges. Here, we present the first conditional generative model
able to produce global climate ensemble simulations that are accurate and
physically consistent. Our model runs at 6-hourly time steps and is shown to be
stable for 10-year-long simulations. Our approach beats relevant baselines and
nearly reaches a gold standard for successful climate model emulation. We
discuss the key design choices behind our dynamics-informed diffusion
model-based approach which enables this significant step towards efficient,
data-driven climate simulations that can help us better understand the Earth
and adapt to a changing climate.
