Learning to sample unprecedented atmospheric rivers

Atmospheric rivers (ARs) are the dominant drivers of hydrological extremes along the western coast of North America, yet the physical upper limits of their intensity remain poorly understood and weakly constrained by the short observational record. While thermodynamic amplification of ARs under climate change is well-documented, the potential for dynamical amplification driven by the wind field remains uncertain and computationally expensive to sample using conventional techniques such as large ensembles of simulations. Here, we address this sampling barrier by leveraging techniques from machine learning, specifically combining a differentiable global climate model with high-resolution regional downscaling to generate storylines of unprecedented AR events in western Canada. By formulating the event generation as an optimal control problem, we compute the gradients of the model’s output to learn minimal, physically plausible perturbations to historical initial states that maximize AR’s associated integrated vapour transport at landfall. These optimized storylines are further dynamically downscaled using a high-resolution regional climate model, producing extreme precipitation events that significantly exceed historical benchmarks.