Case‐Selective Dynamical Downscaling Enables a Tenfold Cost Reduction for Extreme Precipitation Statistics

Abstract High‐resolution climate simulations are essential to study sub‐daily extreme precipitation, yet their computational demands make long‐term large‐ensemble simulations over a large geographical region often infeasible. We introduce a case‐selective dynamical downscaling (CSDD) framework that reconstructs extreme precipitation statistics at convection‐permitting resolution by simulating only periods when extreme rainfall occurs, rather than performing continuous simulations. Using low‐resolution precipitation as a predictor, we identify and downscale time windows associated with extreme events. Applied to a 30‐year regional climate simulation, CSDD reproduces the statistical distribution of 1–6‐hourly precipitation extremes from a full continuous convection‐permitting simulation at roughly 10 of the computational cost. Because cases are independent, they can be executed in parallel, enabling substantial wall‐time reductions. For applications targeting extreme precipitation, notably climate storylines, CSDD provides a physically grounded and computationally efficient way to supplement storylines with reliable extreme‐value statistics, bridging storyline and statistical approaches to climate extremes.