Covered in the Guardian
Human activity such as greenhouse gas emissions and land use change were a key factor in extreme precipitation events such as flooding and landslides around the world, a study has found.
In recent years, there have been numerous instances of flooding and landslides: extreme precipitation, an amount of rainfall or snowfall that exceeds what is normal for a given region, can be a cause of such events.
The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing sub grid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and in homogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.