Projections of extreme precipitation are of considerable interest in a range of design and management applications. These projections, however, can exhibit uncertainty that requires quantification to provide confidence to any application they are used in. This study assesses the uncertainty in projected extreme daily precipitation, separated into model, scenario, and ensemble components using the square root error variance (SREV) rationale. For this, 45 projections of daily precipitation from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are used, consisting of multiple global circulation models and their ensemble members, for a range of Representative Concentration Pathways, allowing assessment across land‐covered areas worldwide. It is found that the uncertainty in dry regions is significantly higher compared to wet regions, raising concerns regarding infrastructure design for the future in arid parts of the world. It is also found that the climate scenarios and initialization contribute significantly to the overall uncertainty, compared to contributions for more nonextreme precipitation simulations. This finding has implications in how design precipitation extremes ought to be projected into the future, with greater attention being paid on a broader selection of emission scenarios and initializations than is the case with projections of nonextreme precipitations.

中文翻译：

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极端日降水量预测中的不确定度量化

在一系列设计和管理应用中，极端降水的预测引起了人们的极大兴趣。但是，这些预测可能会表现出不确定性，需要进行量化以对使用它们的任何应用程序提供置信度。本研究使用平方根误差方差（SREV）评估了预计极端日降水量的不确定性，并将其分为模型，情景和整体分量。 ）原理。为此，使用了“耦合模型比较”项目第5阶段（CMIP5）的45种日降水量预测，其中包括多个全球环流模型及其集合成员，用于一系列“代表浓度路径”，从而可以对全球陆地覆盖区域进行评估。发现与干旱地区相比，干旱地区的不确定性要高得多，引发了有关世界干旱地区未来基础设施设计的担忧。还发现，与更多非极端降水模拟的贡献相比，气候情景和初始化对整体不确定性有显着影响。这一发现对未来如何设计极端降水将产生影响，与非极端降水的预测相比，更加关注排放情景和初始化的更多选择。