Rainfall Frequency Analysis to Estimate Probable Maximum Precipitation Using Stochastic Storm Transposition With Radar–Rain Gauge Data in Japan

Reliable design precipitation estimation is vital for flood risk management and infrastructure design. In Japan, the Depth‐Area‐Duration (DAD) method is widely used for operational Probable Maximum Precipitation (PMP) estimation, but it lacks a probabilistic link to rainfall frequency and does not yield the spatiotemporal rainfall fields needed for hydrological modeling. This study applies Stochastic Storm Transposition (SST), the first application of SST in Japan, to 35 years of high‐resolution Radar/Rain gauge‐Analyzed Precipitation (RA) data to generate probabilistic design precipitation estimates for the Arakawa and Akagawa watersheds. SST‐based precipitation depths are consistently lower than DAD‐based PMP estimates and operational design values adopted by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), used operationally as conservative, roughly 1000‐year benchmarks, while aligning more closely with recent dynamical model‐based PMP studies. At an annual exceedance probability (AEP) of 10 −5 , the SST‐based 48‐h estimate for Arakawa is 9% lower than the DAD‐based value and 16% lower than the MLIT design value; for Akagawa, the SST‐based 12‐h estimate is 20% and 23% lower, respectively. SST‐based results also align closely with recent dynamical model‐based estimates, differing by 3% for Akagawa at AEP = 10 −5 and showing consistent behavior for Arakawa at AEP = 2 × 10 −3 .