极端降水导致的暴雨洪涝灾害是陕西省第二大自然灾害，对社会稳定及经济发展造成了严重的影响。为明确极端降水风险，利用陕西省1969—2020年汛期（5—10月）的日降水数据构建极端降水量、极端降水频次、极端降水强度序列，选取6种极值概率分布模型对构建的序列进行拟合得到陕西省汛期极端降水最优概率分布模型；计算极端降水量以判断未来陕西省极端降水事件变化趋势；基于不同情景得到综合危险性空间分布，对陕西省极端降水综合危险性进行评估。结果表明：（1） Wakeby概率分布为最优概率模型，在3个极端降水指标序列中占比最多，且经过误差分析对比得到Wakeby分布函数是拟合陕西省汛期极端降水指标序列的最优概率分布模型。（2） 计算不同重现期下的极端降水量与现有降水最大值进行对比，发现陕西省大多数地区发生小概率、高危险性的极端降水事件可能性增大。（3） 陕西省汛期极端降水综合危险性整体从陕南至陕北呈南高北低分布，2 a、5 a、10 a、20 a、50 a、100 a一遇情景下危险区域各不相同。随着重现期的增加，低危险区逐渐消失，高危险区面积逐渐增大，100 a重现期下，高危险区面积占比从0增长至22.0%。研究结果可为陕西省极端降水概率分布研究提供参考，为汛期极端降水风险管理和评估工作提供理论依据。

Rainstorms and the resulted floods represent the second most important type of natural disaster in the Shaanxi Province of China. To clearly describe the risk arising from extreme precipitation events, extreme precipitation amount, frequency, and intensity series were constructed using 1969—2020 flood season (May-October) daily precipitation data for Shaanxi Province. Six extreme-value probability distribution models were selected to fit the constructed series to obtain the optimal probability distribution model for flood season extreme precipitation and to evaluate the future trend of extreme precipitation events in Shaanxi Province. The comprehensive risk of extreme precipitation was evaluated based on spatial risk distributions of different scenarios in Shaanxi Province. The results showed that: (1) Through error analysis and comparison, the Wakeby probability distribution was found to be the optimal model for fitting the sequence of extreme precipitation indicators during the flood season in Shaanxi Province, accounting for the largest proportion of extreme values in the three constructed series. (2) Extreme precipitation values with different return periods were calculated and compared with existing maximum precipitation values. An increased probability of low-probability and high-risk extreme precipitation events was found for most areas of Shaanxi Province. (3) The comprehensive risk of extreme precipitation during the flood season was found to be generally high in the south and low in the north of Shaanxi Province. Risk areas differed between scenarios of 2-, 5-, 10-, 20-, 50-, and 100-year return periods. With increasing return periods, the low-risk area gradually reduced and the high-risk area gradually increased. In the 100-year return period scenario, the high-risk area increased from 0 to 22.0%. The study provides a reference for the investigation of extreme precipitation probability distributions in Shaanxi Province and provides a theoretical basis for extreme precipitation risk management and assessment during flood seasons.