Comprehending the changing patterns of flood magnitudes globally, particularly in the context of nonstationary conditions, is crucial for effective flood risk management. This study introduces a unique approach that employs simulated discharge data to unravel these intricate variations. Through a comprehensive analysis of a substantial ensemble of General Circulation Models (GCMs) runoff datasets, we examine the dynamics of nonstationary flood magnitudes on a global scale. A pivotal aspect of our investigation is the development of a reference map, which helps delineate suitable scenarios for applying stationary or nonstationary methods in estimating extreme floods. This map is then employed to compare estimations of 100-year flood magnitudes using both methodologies across specific geographical areas. Our findings distinctly highlight the disparities arising from the use of stationary versus nonstationary approaches for estimating extreme floods. These insights underscore the significance of considering nonstationary for accurate flood risk assessment and mitigation strategies. The practical utility of our reference map in aiding informed decision making for stakeholders and practitioners further underscores its importance. This study contributes to the scholarly understanding of the evolving nature of flood phenomena and provides valuable insights for crafting adaptive measures in response to changing climatic conditions.

This study examined the characteristics of rainfall fields in a monsoon climate using seven years of daily rainfall data from 95 stations in eastern China. The stations were located in a region with a flat, low-lying topography, covering approximately 250 km by 150 km. The spatial and temporal properties of the rainfall fields were analyzed from three perspectives: time scales, spatial distance, and direction. Multiple linear regression was applied to explore linear relationships and predict rainfall amounts at selected sites, considering four time scales: 1 day, 3 days, 7 days, and 15 days. The analysis assumed isotropy when examining the distance–decay in the correlation field. The results showed that the distance–decay of the correlation coefficient followed an exponential pattern, with decay rates decreasing as time scales increased. Spatially, north–south variations were more pronounced than east–west variations, and the correlation extended further in latitude than in longitude, highlighting spatial heterogeneity. The correlation coefficient in the wet season was smaller than for the entire year due to dry days and rainfall variability. Additionally, a multi-site linear model was used to interpolate missing rainfall data. The analysis revealed that the goodness of fit improved over longer time scales. Nevertheless, it was also observed that the error ratio simultaneously increased. This implies that when applying distance-weighted interpolation methods, one must exercise due caution.