Allen Institute's HiRO-ACEv2 is an open-source machine learning system that downscales coarse 100 km climate data to 3 km resolution. It pairs a coarse atmospheric emulator, ACE, with a fine-scale diffusion model, HiROv2, to simulate rare rain, wind, and pressure events. The full pipeline generates global high-resolution output at roughly one-three-hundredth the compute cost of traditional numerical models like X-SHiELD.
The models and tutorial notebooks are available on Hugging Face if you want to inspect extreme weather generation or run regional downscaling yourself. The major catch is that HiROv2 was trained exclusively on X-SHiELD outputs, meaning it currently only works with that specific ACE baseline and cannot yet ingest historical datasets like ERA5 or projected future climates.
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