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Comprehending the correlation between alterations in human‐flood distance and flood fatalities (as well as displacements) is pivotal for formulating effective human adaptive strategies in response to floods. However, this relationship remains inadequately explored in existing global analyses. To address this gap, we examine 910 flood events occurring from 2000 to 2018, resulting in significant numbers of fatalities and displacements. We find that in 53% of countries, humans tend to distance from floods, particularly in the Middle East. Such distancing greatly mitigates flood fatalities and displacements. Simultaneously, in areas with increased flood protection level (FPL), humans are less likely to move away from floods. Furthermore, FPL and human‐flood distance have decreased in regions affected by ice jam‐ and hurricane‐induced floods from 2000 to 2018. Notably, regions with human‐flood distance slightly below the average for a given flood type experience more severe flood fatalities. People have developed effective ways (i.e., distancing from flood and increasing flood protection level) to adapt and minimize the impact of catastrophic floods on exposed populations. However, the relationship between these behaviors and flood fatalities (as well as displacements) is not well understood on a global scale. To address this knowledge gap, we study 910 flood events that occurred between 2000 and 2018, leading to significant fatalities and displacements. We find that in 53% of countries, people tend to move away from flood‐affected areas, especially in the Middle East. This distancing behavior greatly reduces the number of fatalities and displaced individuals caused by floods. Additionally, in areas with higher flood protection level, people are less likely to relocate away from floods. Over the studied period, both flood protection levels and the distance between humans and flood‐prone areas have decreased in regions affected by ice jam‐ and hurricane‐induced floods. Importantly, regions where the distance between humans and floods is slightly below the average for a given flood type experience more severe flood fatalities. These findings could help improve the efficiency of flood control management and the adoption of adaptive policies. 53% of countries witnessed humans distancing themselves from floods between 2000 and 2018, especially those in the Middle EastHumans distancing from the floods leads to a substantial reduction in both flood fatalities and flood displaced populationHumans residing in regions with a high flood protection level tend not to move away from the flood 53% of countries witnessed humans distancing themselves from floods between 2000 and 2018, especially those in the Middle East Humans distancing from the floods leads to a substantial reduction in both flood fatalities and flood displaced population Humans residing in regions with a high flood protection level tend not to move away from the flood

A comprehensive understanding of seasonality in extreme rainfall is essential for climate studies, flood prediction, and various hydrological applications such as scheduling season‐specific engineering works, intra‐annual management of reservoirs, seasonal flood risk mitigation, and stormwater management. To identify seasonality in extreme rainfall and quantify its impact in a theoretically consistent yet practically appealing manner, we investigate a data set of 27 daily rainfall records spanning at least 150 years. We aim to objectively identify periods within the year with distinct seasonal properties of extreme rainfall by employing the Akaike information criterion. Optimal partitioning of seasons is identified by minimizing the within‐season variability of extremes. The statistics of annual and seasonal extremes are evaluated by fitting a generalized extreme value distribution to the annual and seasonal block maxima series. The results indicate that seasonal properties of rainfall extremes mainly affect the average values of seasonal maxima and their variability, while the shape of their probability distribution and its tail do not substantially vary from season to season. Uncertainty in the estimation of the generalized extreme value parameters is quantified by employing three different estimation methods (maximum likelihood, method of moments, and least squares), and the opportunity for joint parameter estimation of seasonal and annual probability distributions of extremes is discussed. The effectiveness of the proposed scheme for seasonal characterization and modeling is highlighted when contrasted to results obtained from the conventional approach of using fixed climatological seasons. Climate studies and hydrological applications, for example, flood risk mitigation, scheduling of season‐specific engineering works, and reservoir management, often require information on the time of occurrence and magnitude of extreme rainfall throughout the year. The seasonal variation of rainfall extremes is investigated in a data set of 27 remarkably long records including at least 150 years of daily measurements. An innovative method is proposed to perform identification of seasons and simulation of extreme rainfall values for each season. Identification of the number of seasons is achieved by evaluating alternative seasonal models according to model selection criteria. Seasonal partitioning is performed by grouping consecutive months with similarly behaving rainfall extremes. To generate synthetic data, extreme value probability distributions are employed. The results indicate that seasonal properties of rainfall extremes mainly affect the average values of seasonal maxima, while the shape of their probability distribution and its tail do not substantially vary from season to season. Uncertainty in the estimated probability distributions is important when comparing results from three different estimation methods, even for long records, where one would expect that uncertainty is limited. The overall effectiveness of the methodology is highlighted when contrasted to the conventional approach of using fixed climatological seasons. The impact of seasonality on the tail index of the probability distribution of extreme rainfall is minimalEstimation uncertainty of extreme rainfall is relevant even when long historical records are availableEstimating seasonality in extreme rainfall by dividing the year in four seasons leads to overfitting and increased uncertainty