Structural warming biases distort extreme rainfall intensification estimates in event attribution

Abstract. Extreme event attribution (EEA) is becoming an increasingly important component of climate change risk assessment and communication. While most EEA methods rely on numerical models, the extent to which model fidelity in representing anthropogenic warming shapes attribution outcomes remains underexplored. Here we identify global-scale biases in leading CMIP6 climate models relative to reanalysis data and show that these biases directly propagate into EEA results. CMIP6 models reproduce the integrated magnitude of anthropogenic warming but systematically distort its three-dimensional structure, underestimating lower-tropospheric warming over land—thus dampening land–sea thermal contrasts—while overestimating upper-tropospheric warming, particularly in the Northern Hemisphere. Consequently, in a storyline-based testbed experiment for the October 2024 Valencia storm (Spain), the response in extreme rainfall rises from ~10 % under CMIP6-derived warming to ~30 % under an observationally constrained signal. This enhanced response is driven by increased low-level moistening, larger convective instability, and strengthened upper-level winds that push precipitation well beyond Clausius–Clapeyron scaling. We also show similar structural mismatches across multiple Northern Hemisphere mid-latitude locations, suggesting that this underestimation is not event-specific. Our results underscore the need to strengthen confidence in attribution methods and provide a robust pathway for constructing observationally constrained counterfactual climates.