Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

Global climate models robustly project intensified extreme precipitation under anthropogenic forcing; however, detecting this in observational records is challenging due to large internal variability. Models exhibit diverse precipitation responses due to varying subgrid-scale process parameterizations. Existing global daily precipitation datasets utilize diverse techniques and inhomogeneous sampling. Previous detection attempts lacked model uncertainty consideration and were regionally limited. This study uses machine learning to account for uncertainties and identify spatiotemporal pattern evolution, revealing a physically interpretable anthropogenic signal detectable across all global observational datasets. This approach efficiently generates multiple lines of evidence supporting anthropogenic influence on global extreme precipitation.

Gavin D. Madakumbura, Chad W. Thackeray, Jesse Norris, Naomi Goldenson, Alex Hall