Increasing evidence suggests that climate change impacts are already observed around the world. Global environmental assessments face challenges to appraise the growing literature. Here we use the language model BERT to identify and classify studies on observed climate impacts, producing a comprehensive machine-learning-assisted evidence map. We estimate that 102,160 (64,958–164,274) publications document a broad range of observed impacts. By combining our spatially resolved database with grid-cell-level human-attributable changes in temperature and precipitation, we infer that attributable anthropogenic impacts may be occurring across 80% of the world’s land area, where 85% of the population reside. Our results reveal a substantial ‘attribution gap’ as robust levels of evidence for potentially attributable impacts are twice as prevalent in high-income than in low-income countries. While gaps remain on confidently attributabing climate impacts at the regional and sectoral level, this database illustrates the potential current impact of anthropogenic climate change across the globe.

越来越多的证据表明，世界各地已经观察到气候变化的影响。全球环境评估面临着评估不断增长的文献的挑战。在这里，我们使用语言模型 BERT 对观察到的气候影响的研究进行识别和分类，生成一个全面的机器学习辅助证据图。我们估计有 102,160 (64,958–164,274) 份出版物记录了广泛的观测影响。通过将我们的空间解析数据库与网格单元级别的温度和降水的人类归因变化相结合，我们推断可归因于人为影响可能发生在世界上 80% 的陆地区域，那里有 85% 的人口居住。我们的结果揭示了一个巨大的“归因差距”，因为在高收入国家中，潜在可归因影响的有力证据水平是低收入国家的两倍。虽然在区域和部门层面对气候影响的自信归因方面仍然存在差距，但该数据库说明了全球人为气候变化的潜在当前影响。