Abstract. Regional Climate Networks (RCNs) are used to identify heat waves and droughts in Germany and two subregions for the summer half years resp. summer seasons of the period 1951 to 2019. RCNs provide information for whole areas (in contrast to the point-wise information from standard indices), the underlying nodes can be distributed arbitrarily, they are easy to 5 construct and provide details otherwise difficult to avail of like extent, intensity and collective behaviour of extreme events. The RCNs were constructed on the regular 0.25 degree grid of the E-Obs data set. The season-wise correlation of time series of daily maximum temperature T_max and precipitation were used to construct the adjacency matrix of the networks. Metrics to identify extremes were the edge density, the 90th percentile of the correlations and the average clustering coefficient, which turned out to be highly correlated; they increased considerably during extreme events. The standard indices for comparison 10 were the effective drought and heat index (EDI and EHI) respectively, based on the same time series, and complemented by other published data. Our results show that the RCNs are able to identify severe extremes in all cases and moderate extremes in most cases. An interesting finding is that during average years, the distribution of the node degrees is close to the Poisson distribution, characteristic of random networks, while for extreme years the distribution is more uniform and heavy tailed.

中文翻译：

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利用区域气候网络识别德国的干旱和热浪

摘要。区域气候网络（RCN）用于确定夏季和夏季德国和两个分区的热浪和干旱。1951年至2019年的夏季。RCN提供了整个区域的信息（与标准索引的逐点信息相反），基础节点可以任意分布，它们易于构建5并提供详细信息，否则难以利用极端事件的程度，强度和集体行为。RCN建立在E-Obs数据集的常规0.25度网格上。每日最高温度T _max的时间序列的季节相关性利用降水和降水来构造网络的邻接矩阵。识别极端的指标是边缘密度，相关性的第90个百分位数和平均聚类系数，事实证明它们之间是高度相关的。在极端事件中，它们显着增加。比较10的标准指数分别是基于相同的时间序列，并由其他已发布的数据补充的有效干旱和高温指数（EDI和EHI）。我们的结果表明，RCN能够在所有情况下识别严重的极端情况，而在大多数情况下能够识别中度的极端情况。一个有趣的发现是，在平均年中，节点度的分布接近于Poisson分布，这是随机网络的特征，而在极端年份，分布更均匀且拖尾。