Contrasting changes in extreme rainfall and river flow as global mean temperature increases

Air can hold more moisture as temperature increases, leading to more extreme rainfall events. Yet, this does not necessarily result in larger river floods. Here we use model projections to explore differences in the response of the atmosphere and catchments to an increase in global mean temperature. For both extreme rainfall and flow, we compute relative changes per °C, often called change factors (CFs) or scaling factors. Unlike some other studies, the multidecadal temperature mean is used instead of the temperature during the extreme event. This allows us to use CFs to produce maps of future changes for any emission scenario and future period.

We relied on rainfall projected by 4 high-resolution GCMs from CMIP6 HighResMIP post-processed using 3 levels spatial smoothing (low to high smoothing). We also used hydrological simulations from 3 global hydrological models (GHMs) forced by 4 GCMs and produced as part of the ISIMIP2b project. We computed changes in the median of annual maxima based on periods of 31 years on 0.25° (HighResMIP) and 0.5° (ISIMIP2b) global grids. Working with two 12-member ensembles enables us to assess uncertainties in future changes.

We found that whilst extreme rainfall is projected to increase over 87% of the land area (ensemble median), only 69% of the land area is projected to show an increase in extreme flow magnitude. Importantly, while there is high model agreement (at least ¾ of the models agree) that extreme rainfall will increase over 76% of the land area, high agreement that future flows will increase is only found over 40% of the land area. We show that these discrepancies are caused by changes in soil moisture and snow pack projected by the GHMs, highlighting the importance of river flood drivers other than extreme rainfall.