SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
arXiv:2605.16163v2 Announce Type: replace-cross Abstract: Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multiscale nonlinear processes that global models cannot explicitly resolve at affordable cost. Global AI weather models can produce skillful medium-range forecasts, but their native 0.25 degrees resolution limits direct use for local hazard applications. Statistical downscaling can help bridge this gap, yet existing approaches often struggle with state-dependent, and especially lead-time-dependen
Advances in AI weather models and statistical downscaling techniques are converging, making high-resolution, localized precipitation forecasting feasible now.
Accurate, kilometer-scale precipitation forecasts are critical for disaster preparedness, agriculture, and infrastructure management in regions with complex terrain like Switzerland.
Global AI weather models can now provide actionable local weather insights through specialized bias correction and downscaling, overcoming previous resolution limitations.
- · Meteorological services
- · Agricultural sector
- · Disaster preparedness agencies
- · Infrastructure planners
Improved local hydrological risk assessment and early warning systems will emerge.
Economic damage from weather-related events in complex terrains may decrease due to better foresight.
This could accelerate the integration of AI models into other highly localized geophysical forecasting systems globally.
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Read at arXiv cs.LG