Future changes in mean and extreme precipitation over the Mediterranean and Sahara regions using bias-corrected CMIP6 models

This study examines the projected changes in mean and extreme precipitation over the Mediterranean (MED) and Sahara (SAH) regions based on the multi-model ensemble mean of the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) datasets. The study employs robust statistical analyses to investigate future changes during 2015–2100 relative to a baseline period (1995–2014), under two Shared Socio-economic Pathways (SSP) scenarios: SSP2-4.5 and SSP5-8.5. Selected indices from the Expert Team on Climate Change and Detection Indices are used in this study. They include those that represent maximum daily precipitation (RX1day), simple daily precipitation intensity (SDII), heavy precipitation days (R10mm), consecutive dry days (CDD), and consecutive wet days (CWD). Historical and projected daily precipitation is first bias-adjusted using a quantile mapping approach before employing them to compute mean and extreme precipitation changes. The results demonstrate that the bias adjustment largely reduces the biases in the modelled mean and extreme precipitation over MED and SAH regions. Projections show a reduction in mean precipitation over most parts of the study region by the end of the 21st century. The areas encompassing Morocco and Algeria, and the Mediterranean area will experience the highest drying. The projected pattern agrees with the “wet gets wetter, dry gets drier” paradigm. The number of consecutive dry days and wet-day intensity are also projected to increase and decrease, respectively. Under SSP5-8.5, significant changes and the largest decrease in SDII attributed to global warming are projected in both regions. The reduction in mean precipitation, coupled with an increase in dry days, is likely to exacerbate the region's droughts and aridity situation and worsen the water scarcity status. Although there are uncertainties in the CMIP simulations, the findings support earlier studies based on varying datasets. This increases confidence in the output for decision-making.