Regional Trajectory Conditioning for Hourly Kilometer-Scale Forecasts from Global Forecast

Global weather forecasts predict large-scale evolution over days, but many applications need hourly, kilometer-scale probabilistic forecasts for a specific region. Regional history describes local conditions at initialization, while an issued global forecast provides information about weather entering the region later. That forecast can differ from the regional future even after spatial alignment, making it guidance rather than an exact regional target. When fine regional fields are generated in shorter windows, each ensemble member still forms an hourly sequence, but the global forecast and regional history do not specify which uncertain regional evolution its windows should share. We introduce Regional Trajectory Conditioning (RTC), a hierarchical conditional diffusion model that samples each member's regional trajectory to coordinate shorter fine windows. Conditioned on the global forecast and regional history, its coarse generator jointly samples that trajectory as hourly averages over blocks of neighboring regional-grid cells. The detail generator uses corresponding segments to produce 3 km fields that preserve those block averages exactly. Overlapping denoising contexts extend this coordinated generation from 24-hour training sequences to 10-day forecasts. Across four U.S. regions, RTC achieves lower state CRPS and trajectory Energy Score than every baseline at both 24-hour and 10-day horizons, and its 10-day scores at hourly lead times are 6.2% and 8.8% lower, respectively, than the lowest baseline score for each metric.