Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (average precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density or by night light intensity -- both proxies of economic activity -- before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, which we share in the open-access Weighted Climate Data Repository web app. Finally, we validate our data through a systematic comparison with those employed in leading climate impact studies.

翻译：尽管多个来源提供了高分辨率网格化气候变量，但在环境与经济研究中，基于经济活动指标加权的国家及区域层面气候数据需求日益普遍。本研究整合不同气候数据源的可用信息，构建了覆盖全球的跨国（GADM0分辨率）与跨区域（GADM1分辨率）空间聚合数据集，涵盖多种气候指标（平均降水量、平均温度、平均SPEI指数）。在聚合前，我们采用人口密度或夜间灯光强度（均为经济活动代理变量）对网格化气候数据进行加权。气候变量以日、月、年三种时间尺度进行测量，覆盖时间跨度（视数据源而定）从1900年（最早）至2023年。我们将所有预处理流程集成于统一框架，并通过开放获取的加权气候数据存储库网页应用进行共享。最后，通过系统性对比当前权威气候影响研究使用的数据集，我们对本数据进行了验证。