High-resolution gridded climate data are readily available from multiple sources, yet climate research and decision-making increasingly require country and region-specific climate information weighted by socio-economic factors. Moreover, the current landscape of disparate data sources and inconsistent weighting methodologies exacerbates the reproducibility crisis and undermines scientific integrity. To address these issues, we have developed a globally comprehensive dataset at both country (GADM0) and region (GADM1) levels, encompassing various climate indicators (precipitation, temperature, SPEI, wind gust). Our methodology involves weighting gridded climate data by population density, night-time light intensity, cropland area, and concurrent population count -- all proxies for socio-economic activity -- before aggregation. We process data from multiple sources, offering daily, monthly, and annual climate variables spanning from 1900 to 2023. A unified framework streamlines our preprocessing steps, and rigorous validation against leading climate impact studies ensures data reliability. The resulting Weighted Climate Dataset is publicly accessible through an online dashboard at https://weightedclimatedata.streamlit.app/.

翻译：高分辨率网格化气候数据可从多种来源便捷获取，然而气候研究与决策制定日益需要结合社会经济因素进行加权的国家及区域特定气候信息。此外，当前分散的数据源与不一致的加权方法加剧了可重复性危机，并损害了科学严谨性。为解决这些问题，我们开发了一个涵盖国家（GADM0）和区域（GADM1）层级的全球综合性数据集，包含多种气候指标（降水、温度、SPEI、阵风）。我们的方法在数据聚合前，采用人口密度、夜间灯光强度、耕地面积及同期人口数量——这些均作为社会经济活动的代理变量——对网格化气候数据进行加权处理。我们整合处理了来自多源的数据，提供1900年至2023年的日度、月度和年度气候变量。统一的框架简化了预处理流程，并通过与前沿气候影响研究的严格验证确保了数据可靠性。最终生成的加权气候数据集可通过在线仪表板公开访问：https://weightedclimatedata.streamlit.app/。