Abstract
Coastal flood hindcasting and forecasting form the foundation of effective risk management in coastal regions. They drive emergency response efforts and inform the design of coastal defenses, adaptation strategies, and evidence-based policy. Global coastal sea level models are traditionally validated using time-series regression metrics (i.e., Root Mean Square Error [RMSE], Mean Absolute Error [MAE], Pearson correlation) at tide gauge sites. However, this approach has two main drawbacks: (1) gauge coverage is geographically sparse outside North America, Northern Europe, and limited parts of Asia, and (2) spatially aggregated metrics obscure prediction errors during extreme sea level events. Here we introduce a novel event-based framework that benchmarks models against a global database of news-reported flood events captured in the Groundsource database. Our curated benchmark of ~33,000 global coastal flood records across 10 years was constructed using a Large Language Model (LLM) calibrated with tide-gauge data to identify coastal floods. Evaluating the performance of two global storm tide reanalyses, namely Global Tide and Surge Model (GTSM) simulations and Environment and Climate Change Canada's (ECCC) hindcast, against our curated flood database, reveals detection rates of only 39% (GTSM) and 43% (ECCC), with nearly half of all flood reportings missed by the two models. Further, we show that spatial aggregation, such as averaging RMSE across the gauges of a country, can mask flood impacts, whereas per-gauge extreme-quantile errors align more closely with performance measured on records of actual floods. This framework offers an essential complementary lens for assessing model quality by providing broad global coverage beyond gauge locations.