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Flooding represents a growing concern for the (re)insurance industry, with precipitation extremes as a key driver of flood risk. Some of the most destructive flood events in 2024 were driven by extreme rainfall occurrences, although with important differences in spatial and temporal scales (e.g. Dubai floods, Ex-Hurricane Debby floods in Canada, Central Europe Floods, Hurricane Helene flooding in Georgia and North Carolina, Valencia floods).Ongoing climate trends introduce additional uncertainty in the estimates of intensity, frequency, and distribution of rainfall extremes, complicating their quantification and risk assessment. Understanding and modelling these extremes is critical for improving flood risk management and financial preparedness.This study investigates rainfall extremes in the United States across various temporal scales, focusing on their role in different types of flood risks. We compare multiple statistical models to estimate extreme precipitation values, including approaches that incorporate climate trends. By analysing spatial and temporal patterns of extremes, we evaluate how well these models capture underlying processes and improve predictive accuracy.Our findings suggest that integrating additional information about climate trends and hydrometeorological processes enhances the accuracy of extreme rainfall estimates, moving in the right direction, although given the rare nature of these extremes looking at historical data alone leaves space for future unexpected outcomes. These results provide valuable insights for improving catastrophe models and stress-testing (re)insurance portfolios.