We analyze data of 27 global climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), and examine projected changes in temperature and precipitation over the African continent during the twenty-first century. The temperature and precipitation changes are computed for two future time slices, 2030–2059 (near term) and 2070–2099 (long term), relative to the present climate (1981–2010), for the entire African continent and its eight subregions. The CMIP6 multi-model ensemble projected a continuous and significant increase in the mean annual temperature over all of Africa and its eight subregions during the twenty-first century. The mean annual temperature over Africa for the near (long)-term period is projected to increase by 1.2 °C (1.4 °C), 1.5 °C (2.3 °C), and 1.8 °C (4.4 °C) under the Shared Socioeconomic Pathways (SSPs) for weak, moderate, and strong forcing, referenced as SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The future warming is not uniform over Africa and varies regionally. By the end of the twenty-first century, the largest rise in mean annual temperature (5.6 °C) is projected over the Sahara, while the smallest rise (3.5 °C) is over Central East Africa, under the strong forcing SSP5-8.5 scenario. The projected boreal winter and summer temperature patterns for the twenty-first century show spatial distributions similar to the annual patterns. Uncertainty associated with projected temperature over Africa and its eight subregions increases with time and reaches a maximum by the end of the twenty-first century. On the other hand, the precipitation projections over Africa during the twenty-first century show large spatial variability and seasonal dependency. The northern and southern parts of Africa show a reduction in precipitation, while the central parts of Africa show an increase, in future climates under the three reference scenarios. For the near (long)-term periods, the area-averaged precipitation over Africa is projected to increase by 6.2 (4.8)%, 6.8 (8.5)%, and 9.5 (15.2)% under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The median warming simulated by the CMIP6 model ensemble remains higher than the CMIP5 ensemble over most of Africa, reaching as high as 2.5 °C over some regions, while precipitation shows a mixed spatial pattern.

We examine the ability of an ensemble of 10 Regional Climate Models (RCMs), driven by ERA-Interim reanalysis, in skillfully reproducing key features of present-day precipitation and temperature (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) over West Africa. We explore a wide range of time scales spanning seasonal climatologies, annual cycles and interannual variability, and a number of spatial scales covering the Sahel, the Gulf of Guinea and the entire West Africa. We find that the RCMs show acceptable performance in simulating the spatial distribution of the main precipitation and temperature features. The occurrence of the West African Monsoon jump, the intensification and northward shift of the Saharan Heat Low (SHL), during the course of the year, are shown to be realistic in most RCMs. They also capture the mean annual cycle of precipitation and temperature, including, single and double-peaked rainy seasons, in terms of timing and amplitude over the homogeneous sub-regions. However, we should emphasize that the RCMs exhibit some biases, which vary considerably in both magnitude and spatial extent from model to model. The interannual variability of seasonal anomalies is best reproduced in temperature rather than precipitation. The ensemble mean considerably improves the skill of most of the individual RCMs. This highlights the importance of performing multi-model assessment in properly estimating the response of the West African climate to global warming at seasonal, annual and interannual time scales.

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We provide an assessment of future daily characteristics of African precipitation by explicitly comparing the results of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models, specifically highlighting the similarities and inconsistencies between them. Results for seasonal mean precipitation are not always consistent amongst ensembles: in particular, global models tend to project a wetter future compared to regional models, especially over the Eastern Sahel, Central and East Africa. However, results for other precipitation characteristics are more consistent. In general, all ensembles project an increase in maximum precipitation intensity during the wet season over all regions and emission scenarios (except the West Sahel for CORE) and a decrease in precipitation frequency (under the Representative Concentration Pathways RCP8.5) especially over the West Sahel, the Atlas region, southern central Africa, East Africa and southern Africa. Depending on the season, the length of dry spells is projected to increase consistently by all ensembles and for most (if not all) models over southern Africa, the Ethiopian highlands and the Atlas region. Discrepancies exist between global and regional models on the projected change in precipitation characteristics over specific regions and seasons. For instance, over the Eastern Sahel in July–August most global models show an increase in precipitation frequency but regional models project a robust decrease. Global and regional models also project an opposite sign in the change of the length of dry spells. CORE results show a marked drying over the regions affected by the West Africa monsoon throughout the year, accompanied by a decrease in mean precipitation intensity between May and July that is not present in the other ensembles. This enhanced drying may be related to specific physical mechanisms that are better resolved by the higher resolution models and highlights the importance of a process-based evaluation of the mechanisms controlling precipitation over the region.

This paper used the International Centre for Theoretical Physics (ICTP) Regional Climate Model, Version 3 (RegCM3) and rain gauge data selected from the Ghana Meteorological Agency (GMet) from 1990 to 2008 to investigate the extent and nature of variability in the annual rainfall and pattern of the raining seasons in Ghana. In the study, six meteorological stations selected from three rainfall distribution zones according to the divisions of the GMet were used to study the pattern of rainfall and its departure from the normal trend. The study also assessed the performance of the RegCM3 simulation with reference to the observed gauge data. Results confirmed the unimodal nature of the rainfall annual cycle over the northern belt and bi-modal rainfall nature over the middle and southern belts of Ghana. Negative departures of rainfall implying consistent downward trend were observed at all the stations. Our analysis showed that RegCM3 captured the average rainfall over Ghana but demonstrated an underestimation as compared to the observed gauge data. The model also had difficulty stimulating the departures accurately in direction and in magnitude in all the stations except for Accra where RegCM3 simulated the right direction of the departures.

Various sectors of the country's economy – agriculture, health, energy, among others – largely depend on climate information, hence availability of quality climate data is very essential for climate‐impact studies in these sectors. In this paper, a monthly rainfall database (GMet v1.0) has been developed at a 0.5° × 0.5° spatial resolution, from 113 Ghana Meteorological Agency (GMet) gauge network distributed across the four agro‐ecological zones of Ghana, and spanning a 23‐year period (1990–2012). The datasets were first homogenized with quantile‐matching adjustments and thereafter, gridded at a spatial resolution of 0.5° × 0.5° using Minimum Surface Curvature with tensioning parameter, allowing for comprehensive spatial fields assessment on the developed dataset. Afterwards, point‐pixel validation was performed using GMet v1.0 against gauge data from stations that were earlier excluded due to large datagaps. This proved the reliability of GMet v1.0, with high and statistically significant correlations at 99% confidence level, and relatively low biases and rmse. Furthermore, GMet v1.0 was compared with GPCC and TRMM rainfall estimates, with both products found to adequately mimick GMet v1.0, with high correlations which are significant at 99% confidence level, low biases and rmse. In addition, the ratio of 90th – percentile provided fairly similar capture of extremes by both TRMM and GPCC, in relation to GMet v1.0. Finally, based on annual rainfall totals and monthly variability, k‐means cluster analysis was performed on GMet v1.0, which delineated the country into four distinct climatic zones. The developed rainfall data, when officially released, will be a useful product for climate impact and further rainfall validation studies in Ghana.