等距
匹配(统计)
累积分布函数
降水
稳健性(进化)
计算机科学
功能(生物学)
统计
数学
概率密度函数
气象学
物理
化学
几何学
生物化学
进化生物学
生物
基因
摘要
Abstract Equidistant cumulative distribution function (CDF) matching has been used frequently in recent studies to bias‐correct raw modeled precipitation. However, this brief discussion shows that negative precipitation will result from applying this method. A feasible alternative to avoid this problem is to use equiratio CDF matching as proposed in this study. A real‐world assessment based on Coupled Model Inter‐comparison Project 5 ( CMIP5 ) confirms the effectiveness and robustness of equiratio CDF matching in systematically removing biases in modeled precipitation. Our conclusions here will require a re‐examination of the relevant literature in which equidistant CDF matching is used to bias‐correct precipitation.
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