Spatially homogenous precipitation regions were delineated for the contiguous United States using a gridded data set of daily precipitation. Seasonal means (1981–2010) of four variables, together characterizing seasonal precipitation, were computed and subjected to a principal component analysis (PCA). PCA reduced the original 30,665 grid cells by sixteen precipitation variables (four variables, four seasons) in the data set. The standardized scores of the three retained principal components, which together retain 78.4 percent of the original data set’s variance, were then subjected to three agglomerative hierarchical clustering techniques. Using an objective method, several cluster solutions were examined, and the average linkage thirteen-cluster solution was deemed optimal. The average linkage solution was then subjected to a k-means partitioning technique under the premise that objects are not considered for reassignment during agglomerative hierarchical cluster procedures. The result is fifteen precipitation regions across the contiguous United States. Results indicate that the regions successfully minimize intraregion variability and maximize interregion variability when compared to the nine climate regions defined by the United States National Centers for Environmental Information. It is therefore suggested that the regions defined by this work will better serve research aimed at an improved understanding of long-term hydroclimate change and variability at regional to synoptic scales across the United States.

使用每日降水量的网格数据集，为连续的美国划定了空间均匀的降水量区域。计算了四个变量的季节性平均值（1981-2010），这些变量共同表征了季节性降水，并进行了主成分分析（PCA）。PCA通过数据集中的十六个降水变量（四个变量，四个季节）减少了原始的30,665个网格单元。然后，对三个保留的主成分的标准化分数（总共保留原始数据集方差的78.4％）进行了三种聚集的层次聚类技术。使用一种客观的方法，检查了几个群集解决方案，并认为平均链接十三群集解决方案是最佳的。然后将平均连接溶液进行k- means分区技术的前提是，在聚集层次聚类过程中不考虑重新分配对象。结果是整个美国连续有15个降水区。结果表明，与美国国家环境信息中心定义的九个气候区域相比，这些区域成功地将区域内变异性最小化，并将区域间变异性最大化。因此，建议这项工作定义的地区将更好地为旨在更好地了解美国各地地区到天气尺度的长期水气候变化和变化的研究服务。