In recent years, attribution analyses have been applied increasingly quickly following an event, with some techniques using forecasts generated before the event. In addition, event attribution frameworks have been applied to the underlying physical causes of extremes ( 2, 17, 18), including atmospheric circulation patterns, atmospheric water vapor ( 4), ocean heat content ( 23), and wildfire risk factors. ( 1), attribution analyses have been published for many types of events ( 2), including heatwaves, cold snaps, heavy rainfall, floods, droughts, tropical cyclone precipitation, storm surge flooding, and extremely low Arctic sea ice. Hence, the use of temporally lagged periods for attribution-and, more broadly, for extreme event probability quantification-can cause underestimation of historical impacts, and current and future risks. These discrepancies are most explained by increases in climate forcing between the attribution and verification periods, suggesting that 21st-century global warming has substantially increased the probability of unprecedented hot and wet events. The underestimation is reflected in discrepancies between probabilities predicted during the attribution period and frequencies observed during the out-of-sample verification period. This underestimation is particularly pronounced for hot and wet events, with greater uncertainty for dry events. This framework reveals that previously published results based on a 1961–2005 attribution period frequently underestimate the influence of global warming on the probability of unprecedented extremes during the 2006–2017 period. This study presents a framework for such verification. Independent verification of anthropogenic influence on specific extreme climate events remains elusive.
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