Global climate models produce large increases in extreme precipitation when subject to anthropogenic forcing, but detecting this human influence in observations is challenging. Large internal variability makes the signal difficult to characterize. Models produce diverse precipitation responses to anthropogenic forcing, mirroring a variety of parameterization choices for subgrid-scale processes. And observations are inhomogeneously sampled in space and time, leading to multiple global datasets, each produced with a different homogenization technique. Thus, previous attempts to detect human influence on extreme precipitation have not incorporated internal variability or model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods, we find a physically interpretable anthropogenic signal that is detectable in all global datasets. Detection occurs even when internal variability and model uncertainty are taken into account. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in extreme precipitation.