This article compares the performances of three fitting methods (SLmom, S1NCM, and S3NCM) to account for temporal characteristics of Annual Maximum Precipitations (AMPs) on daily and sub‐daily time scales using scaling General Extreme Value (GEV) distribution at a local site. Based on simple scaling properties of AMPs, the temporal downscaling model (called Scaling‐GEV) with parameter estimation methods are used to estimate sub‐daily AMPs from observed daily data. The feasibility and accuracy of the suggested method were assessed using rainfall data available from Dorval in Quebec (Canada) and Seoul (South Korea) for the period 1961–1990. Presence of simple scaling properties of AMPs for two stations has shown that it is feasible to use the temporal downscaling method for describing the linkage between AMPs of different time scales. Numerical and graphical analyses revealed that the Scaling‐GEV distribution by the Three‐Non central moments (NCM) method (S3NCM) provides the most accurate estimates compared to observed data amongst three fitting methods. In addition, this study suggested a modified bootstrap technique to determine confidence intervals (CIs) CIs of extreme rainfall series using the simple scaling properties of extreme rainfalls and only daily AMPs. Although the CIs were constructed by only daily AMPs and the simple scaling properties, the observed sub‐daily AMPs are generally within the 95% CI estimated.

中文翻译：

---

使用Scaling-GEV分布模型表征极端降雨并建立IDF曲线的置信区间

本文比较了三种拟合方法（SLmom，S1NCM和S3NCM）的性能，这些方法使用缩放本地站点的一般极值（GEV）分布来说明每日和次日尺度上的年度最大降水（AMP）的时间特征。 。基于AMP的简单缩放属性，使用带有参数估计方法的时间缩减模型（称为Scaling-GEV）可从观察到的每日数据估计次日AMP。利用从魁北克（加拿大）多尔瓦尔（Dorval）和加拿大首尔（韩国）获得的1961-1990年降雨数据，评估了该方法的可行性和准确性。两个站的AMP的简单缩放属性的存在已表明，使用时间缩减方法来描述不同时标的AMP之间的联系是可行的。数值和图形分析表明，与三种拟合方法相比，通过三非中心矩（NCM）方法（S3NCM）进行的Scaling-GEV分布提供了最准确的估计。此外，这项研究提出了一种改进的自举技术，该技术可以使用极端降雨的简单缩放属性（仅每天使用AMP）来确定极端降雨序列的置信区间（CI）CI。尽管配置项仅由每日AMP和简单的缩放属性构成，但观察到的次日级AMP通常在估计的95％CI之内。这项研究提出了一种改良的自举技术，该技术可以使用极端降雨的简单缩放属性和仅使用每日AMP来确定极端降雨序列的置信区间（CI）CI。尽管配置项仅由每日AMP和简单的缩放属性构成，但观察到的次日级AMP通常在估计的95％CI之内。这项研究提出了一种改良的自举技术，该技术可以使用极端降雨的简单缩放属性和仅每日AMP来确定极端降雨序列的置信区间（CI）CI。尽管配置项仅由每日AMP和简单的缩放属性构成，但观察到的次日级AMP通常在估计的95％CI之内。