ABSTRACT A downscaling model capable of explaining the temporal and spatial variability of regional hydroclimatic variables is essential for reliable climate change studies and impact assessments. This study proposes a novel statistical approach based on generalized hierarchical linear model (GHLM) to downscale precipitation from the outputs of general circulation models (GCMs) at multiple sites. GHLM partitions the total variance of precipitation into within- and between-site variability allowing for transferring information between sites to develop a regional downscaling model. The methodology is demonstrated by downscaling precipitation using the outputs of eight GCMs in Lake Urmia basin in northwestern Iran. Multi-model ensemble simulations are merged and bias-corrected using Bayesian model averaging and equidistant quantile mapping, respectively. The results of this study show projected declining trends in precipitation resulting in approximately 11.2% and 15.3% decrease during 2060–2080 compared to the historical period of 1985–2005 considering representative concentration pathways (RCPs) 4.5 and 8.5, respectively.

中文翻译：

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使用广义分层线性模型对降水进行多站点统计降尺度：以濒危的乌尔米亚湖盆地为例

摘要 能够解释区域水文气候变量时空变异的降尺度模型对于可靠的气候变化研究和影响评估至关重要。本研究提出了一种基于广义分层线性模型 (GHLM) 的新统计方法，以从多个站点的一般环流模型 (GCM) 的输出中缩减降水量。GHLM 将降水的总方差划分为站点内和站点间的可变性，允许站点之间传输信息以开发区域降尺度模型。该方法通过使用伊朗西北部乌尔米亚湖盆地的八个 GCM 的输出降尺度降水来证明。使用贝叶斯模型平均和等距分位数映射合并和校正多模型集成模拟，分别。本研究的结果显示，考虑到代表性浓度路径 (RCP) 4.5 和 8.5，与 1985 年至 2005 年的历史时期相比，预计降水量下降趋势将导致 2060 年至 2080 年期间减少约 11.2% 和 15.3%。