Global estimates of 100-year return values and confidence intervals of daily precipitation for different data sets

High-impact river floods are often caused by very extreme precipitation events with return periods of several decades or centuries, and the design of flood protection measures thus relies on reliable estimates of the corresponding return values. However, calculating such return values from observations is associated with large statistical uncertainties due to the limited length of observational time series. Here, estimates of 100-year return values of daily precipitation on a global grid based on a large data set of model-generated precipitation events from ensemble weather prediction are presented, in which statistical uncertainties of the return values are substantially reduced compared to observational estimates.

高影响河流洪水往往由重现期达数十年至数百年的极端强降水事件引发，因此防洪防护措施的设计需依赖对应重现值的可靠估算结果。然而，受限于观测时间序列的长度，基于观测数据计算此类重现值往往伴随较大的统计不确定性。本研究基于集合天气预报生成的海量模式降水事件数据集，给出了全球网格尺度下日降水百年重现值的估算结果，相较于基于观测数据的估算结果，该结果的重现值统计不确定性已大幅降低。