Spatiotemporal variations of global precipitation concentration and potential links to flood-drought events in past 70 years

The evaluation of precipitation events is crucial for predicting severe droughts and floods, particularly in the context of global warming. This study presents a comprehensive spatiotemporal analysis of the global precipitation concentration index (CI) from 1950 to 2020, comparing CI with nine extreme precipitation indices to assess its applicability. The world map was divided into three distinct regions related to flood-drought events using the standardized precipitation index (SPI). Key findings include: 1) CI exhibited significant spatial heterogeneity, with elevated values in seasonally warm coastlines and arid regions. Seasonal changes in CI were not synchronized, particularly lower in Northern Hemisphere winters, while the Southern Hemisphere displayed minor seasonal variation, alongside a long-term growth trend in approximately 36.5 % of the global area. 2) CI's spatiotemporal variations helped delineating regional precipitation patterns, revealing a strong correlation (R² = 0.87) with the extreme precipitation index R95c, but a negligible relationship with total precipitation (P) (R² = -0.05). This facilitated the global partitioning into three regions: Humidity Amplification Region (I), Dryness Intensification Region (II), and Climate Fluctuation Region (III). 3) Flood-drought events were potentially linked to variations in CI and P, where In Region I experienced increased flood risk due to rising CI and P, Region II faced heightened drought risk from rising CI and declining P, and Region III showed opposing effects on floods due to reduced CI and increased P, with P variability significantly influencing flood frequency. 4) The main atmospheric circulation factors varied by region, typically including the Antarctic Oscillation (AAO), Atlantic Multi-decadal Oscillation (AMO) and Arctic Oscillation (AO), with AAO being most significant. This study highlights the potential of CI in improving the prediction of drought and flood risks, emphasizing the need for enhanced regional data and dynamic modeling to better assess the impacts of climate change on precipitation patterns and hydrological extremes.