Francesco Immorlano

Climate simulations are costly and biased, while correcting them too strongly toward observations risks erasing structural differences among models. A conditional diffusion model trained on seventeen CMIP6 models and ERA5 reanalysis steers each emulated model distribution toward observations at sampling time with an adjustable strength, chosen in perfect-model experiments with withheld simulated futures and decreasing with projection horizon and forcing. Across seventeen synthetic targets, guidance more than halves median biases in the 1st and 99th percentiles of daily temperature and brings CMIP6's roughly threefold historical overestimate of extreme-heat coverage close to observations.

Climate simulations are costly and biased, while correcting them too strongly toward observations risks erasing structural differences among models. A conditional diffusion model trained on seventeen CMIP6 models and ERA5 reanalysis steers each emulated model distribution toward observations at sampling time with an adjustable strength, chosen in perfect-model experiments with withheld simulated futures and decreasing with projection horizon and forcing. Across seventeen synthetic targets, guidance more than halves median biases in the 1st and 99th percentiles of daily temperature and brings CMIP6's roughly threefold historical overestimate of extreme-heat coverage close to observations.