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

Abstract

This study examines the projected changes in mean and extreme precipitation over the Mediterranean (MED) and Sahara (SAH) regions based on the multimodel 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)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(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.

Projection of climate change impacts on extreme temperature and precipitation in Central Poland

“…Finally, the CDFs of observed variables can be used to estimate corrected values for future periods. The detailed information related to the QM can be found in the literature 43 – 46 . The capability of the QM bias-correction method has been approved in previous studies 47 – 51 .…”

Future Land Use and Flood Risk Assessment in the Guanzhong Plain, China: Scenario Analysis and the Impact of Climate Change

“…For higher accuracy, the BCSD method was chosen to downscale the data after ensemble averaging, and it can be seen that BCSD can completely carry out downscaling and correction work. Zhu [56,57] used the model ensemble averaging method to validate the CMIP6 precipitation data for the Tibet Plateau and the Yangtze River Basin, and the results showed that the model ensemble averaging method can reduce model errors well, but further corrections are needed. Eum [58] ranked four downscaling methods, BCSD, the bias-correction/constructed analog, multivariate adaptive constructed analogs (MACA), and the bias-correction/climate imprint, based on performance metrics using the TOPSIS; the results showed that MACA and BCSD have considerable skills regarding the time series correlation criteria, while BCSD is superior to the other methods regarding the distribution and extreme value correlation criteria.…”

Evaluation and correction analysis of the regional rainfall simulation by CMIP6 over Sudan

“…Furthermore, future changes in rainfall across this region must be detected and understood by stakeholders in resource management and planning, as the variability of the future climate is a major concern . Since the primary tools of the analyses and determining what climate we are likely to have in the near and not-so-near future use dynamical downscaling with regional climate models (RCMs) and global climate models (GCMs) (Maroneze et al, 2014), the evaluation of climate models is considered as essential for providing model-based climate data (Babaousmail et al, 2022). Several climate modeling organizations have run climate simulations for the future using various IPCC emission scenarios .…”