Regional climate networks (RCNs) are used to identify heatwaves and droughts in Germany and two subregions for the summer half-years and 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 construct, and they provide details otherwise difficult to access, like temporal and spatial extent and localisation of extreme events; this makes them suitable for the statistical analysis of climate model output. The RCNs were constructed on the regular 0.25^∘ grid of the E-OBS data set. The season-wise correlation of the time series of daily maximum temperature T_max and precipitation were used to construct the adjacency matrix of the networks. Based on the results of a sensitivity study, we used the edge density, which increases significantly during extreme events, as the main metrics to characterise the network structure. The standard indices for comparison were the Effective Drought Index and Effective 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 generally able to identify severe and moderate extremes and can differentiate between regions and seasons.

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

区域气候网络（RCN）用于识别德国和两个子区域在1951年至2019年夏季半年和夏季的热浪和干旱。RCN提供整个区域的信息（与来自标准索引），基础节点可以任意分布，易于构建，并且提供了其他方式难以访问的细节，例如时间和空间范围以及极端事件的定位；这使它们适合于气候模型输出的统计分析。RCN建立在E-OBS数据集的常规^0.25∘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建网络的邻接矩阵。基于敏感性研究的结果，我们使用了边缘密度（它是在极端事件中显着增加的边缘）作为表征网络结构的主要指标。用于比较的标准指数分别是在相同的时间序列基础上并结合其他已发布的数据的有效干旱指数和有效热量指数（EDI和EHI）。我们的结果表明，RCN通常能够识别严重和中度的极端情况，并且可以区分地区和季节。