Bias Correction Can Strongly Shape California's Projected Megaflood Risk

Abstract

A California “megaflood”—a weeks‐long cluster of storms with centuries‐long return times—would be catastrophic, yet its timing and magnitude under climate change remains uncertain. A central source of uncertainty is whether Earth System Model boundary conditions should be bias corrected before dynamical downscaling. Here, we analyze a large ensemble of downscaled simulations to quantify how this uncertainty shapes California's megaflood risk. Bias correction is necessary to reproduce observed storm statistics and strongly influences both the megafloods historical and future characteristics. Without correction, storm precipitation variability is inappropriately inflated, delaying the emergence of extreme precipitation signals by decades. Further, megaflood intensity estimates based on statistically extended observations are at least 10% drier than explicit computations derived from sufficiently sized storm populations. Our results show that bias correction choices crucially govern projections of rare, high‐impact events, with direct implications for infrastructure design/planning and water management in California.