Future rainfall extremes are projected to increase with global warming according to theory and climate models, but common (annual) and rare (decennial or centennial) extremes could be affected differently. Here, using 25 models from the Coupled Model Intercomparison Project Phase 6 driven by a range of plausible scenarios of future greenhouse gas emissions, we show that the rarer the event, the more likely it is to increase in a future climate. By the end of this century, daily land rainfall extremes could increase in magnitude between 10.5% and 28.2% for annual events, and between 13.5% and 38.3% for centennial events, for low and high emission scenarios respectively. The results are consistent across models though with regional variation, but the underlying mechanisms remain to be determined.

Africa is severely affected by floods, with an increasing vulnerability to these events in the most recent decades. Our improved preparation against and response to this hazard would benefit from an enhanced understanding of the physical processes at play. Here, a database of 399 African stream gauges is used to analyze the seasonality of observed annual maximum flood, precipitation and soil moisture between 1981 and 2018. The database includes a total of 11,302 flood events, covering most African regions. The analysis is based on directional statistics to compare the annual maximum river flood with annual maximum rainfall and soil moisture. The results show that the annual maximum flood in most areas is more strongly linked to the annual peak of soil moisture than of annual maximum precipitation. In addition, the interannual variability of flood magnitudes is better explained by the variability of annual maximum soil moisture than by the variability in the annual maximum precipitation. These results have important implications for flood forecasting and the analysis of the long‐term evolution of these hydrological hazards in relation with their drivers.

Global warming impacts the hydrological cycle, affecting the seasonality and timing of extreme precipitation. Understanding historical changes in extreme precipitation occurrence is crucial for assessing their impacts. This study uses relative entropy to analyze historical changes in seasonality and timing of extreme daily precipitation occurrences on the global domain for 63 years of fifth generation of the European Reanalysis reanalysis data. Our analysis reveals distinct regional patterns of change. During the second half of the 20th century, Africa and Asia experienced high clustering of precipitation extremes. Over the past 60 years, clustering increased in Africa while becoming more spread out in Asia. North America and Australia had initially lower clustering and showed slight increases over time. Extreme events in extra‐tropical land regions mainly occurred in summer, with modest shifts in timing. These findings have implications for risk assessments of natural hazard like flash floods and landslides, emphasizing the necessity for region‐specific adaptation strategies.

Sufficient and accurate hydro-meteorological data are essential to manage water resources. Recently developed global reanalysis datasets have significant potential in providing these data, especially in regions such as Southern Africa that are both vulnerable and data poor. These global reanalysis datasets have, however, not yet been exhaustively validated and it is thus unclear to what extent these are able to adequately capture the climatic variability of water resources, in particular for extreme events such as floods. This article critically assesses the potential of a recently developed global Water Resources Reanalysis (WRR) dataset developed in the European Union's Seventh Framework Programme (EU-FP7) eartH2Observe (E2O) project for identifying floods, focussing on the occurrence of floods in the Limpopo River basin in Southern Africa. The discharge outputs of seven global models and ensemble mean of those models as available in the WRR dataset are analysed and compared against two benchmarks of flood events in the Limpopo River basin. The first benchmark is based on observations from the available stations, while the second is developed based on flood events that have led to damages as reported in global databases of damaging flood events. Results show that, while the WRR dataset provides useful data for detecting the occurrence of flood events in the Limpopo River basin, variation exists amongst the global models regarding their capability to identify the magnitude of those events. The study also reveals that the models are better able to capture flood events at stations with a large upstream catchment area. Improved performance for most models is found for the 0.25° resolution global model, when compared to the lower-resolution 0.5° models, thus underlining the added value of increased-resolution global models. The skill of the global hydrological models (GHMs) in identifying the severity of flood events in poorly gauged basins such as the Limpopo can be used to estimate the impacts of those events using the benchmark of reported damaging flood events developed at the basin level, though this could be improved if further details on location and impacts are included in disaster databases. Large-scale models such as those included in the WRR dataset are used by both global and continental forecasting systems, and this study sheds light on the potential these have in providing information useful for local-scale flood risk management. In conclusion, this study offers valuable insights in the applicability of global reanalysis data for identifying impacting flood events in data-sparse regions.