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Precipitation is the main component of the hydrological cycle. It has a significant effect on the ecosystem especially, irrigation and drainage system design and management, crop production, and flood and drought management. In this study, 11 global circulation models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) were investigated under three shared socioeconomic pathway (SSP) scenarios for precipitation simulation and projection in the future period (2021–2050). Then, the results were compared with the base period (1985–2014). The research was conducted in the MENAP region. The evaluation of GCMs’ performances by the Taylor diagram, R2, MSE, MAE, and RMSE indices showed that among the 11 models, the MPI-ESM1-2-HR model with an average R2 and RMSE of 0.6 and 18.9, respectively, was more accurate than other models in precipitation simulation in the entire region. The projection of precipitation indicated that the precipitation will mainly decrease except in areas such as the Black Sea, Mediterranean, and Red Sea coastal areas as well as mountainous and higher altitude regions in the eastern part of the study area. In addition, highest decrease rates will happen in the Middle East countries, Afghanistan, Morocco, Algeria, and Sudan. Based on different scenarios in the MENAP region, precipitation will vary between −77.3 and + 51.1 mm compared to the base period. Moreover, the lowest and highest precipitation changes were estimated based on SSP1-2.6 and SSP5-8.5 scenarios. According to the various scenarios, the amount of precipitation in the future period will decrease compared to the historical period in most parts of the areas under study.

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  • Analysis of the 11 global circulation models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6).

  • Using the three shared socioeconomic pathway (SSP) scenarios.

  • Study of 220 meteorological stations in the MENAP (Middle East, North Africa, Afghanistan, and Pakistan) region.

  • Assessment of CMIP6 model's performance by the Taylor diagram, R2, MSE, RMSE, and MAE.

  • Using the grid data of ERA5 and GCMs.

climate change, CMIP6, MENAP, precipitation, SSPs
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The climate change process, especially temperature and precipitation change, is one of the most important subjects in environmental sciences. Climate change has massive socioeconomic effects due to the dependency of human activities, agriculture, and industries on climatic parameters (such as water) to achieve sustainability (Goudarzi et al. 2018). Given the increase in greenhouse gases and higher intensity in climatic parameters in future periods, this phenomenon negatively affects various systems and activities, including water resources, environment, industry, health, and agriculture (IPCC 2007). Meanwhile, precipitation is the most significant atmospheric and hydrological variable connecting the climate and the ground surface process, and its positive and negative anomalies cause floods and droughts. Therefore, this parameter must be precisely estimated and predicted in different regions, especially in those countries where the economy relies on the agriculture sector (Goudarzi et al. 2016). In recent decades, extensive research has been conducted on long-term and medium-term predictions (monthly and seasonal) of precipitation in different parts of the world. According to the results of climate change studies in the Mediterranean Sea basin, there was a relationship between temperature increases, precipitation decreases, water deficiency, and increases in wildfire susceptibility (Panol & Loret 1998). Based on reports (Lane et al. 1999), climate change caused hydrologic cycle changes over the past few decades worldwide, in such a way that precipitation and surface flows were higher in high and middle latitudes, but lower in lower latitudes. Moreover, the possibility of climate extreme events, such as floods and droughts, has increased. Therefore, the study of precipitation changes and projections will lead the future strategy and policy in water resources management (Hardy 2003). Global circulation models (GCMs) are the most important numerical coupled models and robust tools in assessing the effects of climate change and simulating and displaying different atmospheric systems, ocean, land, and ice-sea level (Fowler et al. 2007). Additionally, these models simulate the global climate responses to the greenhouse gases concentration as well as future climatic scenarios (IPCC 2013). Up to now, several GCMs have been developed. The differences in the structure of these models and initial implementation conditions might produce different results for an identical emission scenario; therefore, regional studies require evaluation of the output of these models (Kay et al. 2009). In this concept, Sarkar et al. (2015) predicted temperature and precipitation changes using GCMs in India. Their results showed that precipitation would decrease from 9 to 27% for future periods. Hyun Cha et al. (2016) investigated summer precipitation changes in Korea using representative concentration pathway (RCP) scenarios. The authors found out that precipitation decreased while its intensity increased. Leong Tan et al. (2017) evaluated the effects of climate change on water resources in Malaysia. Their results highlighted a monthly increasing precipitation in the wet season and decreasing precipitation in the dry season. Ferreira et al. (2018) examined the effects of climate change on summer precipitation in the southeast of the United States, indicating a significant increase in precipitation due to increasing temperature and water vapor flux. Nilawar & Waikar (2019) studied the effects of climate change on the Purna River flow in India using RCP scenarios. Their findings showed that temperature and precipitation would increase in future periods. Jiang et al. (2020) performed a projection of precipitation change based on 15 GCMs from CMIP6 under four SSP scenarios in central Asia. The authors mentioned an increase in average annual precipitation based on all scenarios. Babaousmail et al. (2021) evaluated the performance of 15 CMIP6 models in examining spatial and temporal variations in precipitation in North Africa. Their results confirmed that the EC-Earth3-Veg model had the best performance in the study area; however, it was recommended to use several superior models to examine precipitation changes. Majdi et al. (2022) projected temperature and precipitation changes in the Middle East and North Africa regions using the average 23 GCMs and two SSP scenarios. The results indicated an increase in temperature and a decrease in precipitation in most study areas under both scenarios. In most of the previous studies, grid data have been used and less than station-scale data have been used in a wide area. This issue led us to evaluate different models by comparing the data of meteorological stations and modeled, and to increase the projection accuracy by using bias correction methods.

Considering the importance of climate change and its projection in the MENAP region (Middle East, North Africa, Afghanistan, Pakistan, and Turkey), in this research, we investigated different CMIP6 models for precipitation simulation in the historical period (1985–2014) and after choosing the best model, the changes of precipitation in the future period (2021–2050) were projected under different scenarios.

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The study area includes Middle East and North African countries. According to World Bank statistics from January 2021, this area includes 21 countries: Algeria, Bahrain, Djibouti, Egypt, Iraq, Iran, Israel, Jordan, Kuwait, Lebanon, Libya, Malta, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, Sudan, United Arab Emirates, and Yemen (Majdi et al. 2022). In 2003, the International Monetary Fund also used the MENAP word in its statistical analysis, including the same countries of the MENA region, plus Afghanistan and Pakistan. The word MENAP also means the same MENA countries along with the country of Turkey. In order to investigate the precipitation data, 220 meteorological stations were used from different countries of the MENAP region along with Turkey (24 countries). The geographic location of the study area and the meteorological stations are presented in Figure 1.
Figure 1
Geographical location of the study area and meteorological study stations.
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Geographical location of the study area and meteorological study stations.

Figure 1
Geographical location of the study area and meteorological study stations.
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Geographical location of the study area and meteorological study stations.

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The reference data for precipitation is reanalysis data of ERA5 with a resolution of 0.25° × 0.25° obtained from https://cds.climate.copernicus.eu/ website. Its precipitation database is the latest version of the ECMWF Center's forecast series, one of the most significant analytics databases in the world. The bias rate of this database is negligible compared to ground-based data in many parts of the world. Hence, the data can be used alongside ground gauge data and even as an alternative in areas without meteorological stations. Reanalyzed data are obtained by combining the results of short-term simulations of numerical climate models with ground-based observational data (Dee et al. 2011). The ability of the ERA5 database to estimate precipitation was validated by various researchers (Xu et al. 2019; Amjad et al. 2020). Figure 2 show the spatial distribution of precipitation in the study area based on ERA5 data in the historical period (1985–2014). According to Figure 2, the highest precipitation is in southern Sudan, southwest of the Caspian Sea, and northeast of Pakistan in the study area.
Figure 2
The spatial distribution of precipitation (mm) in the study area based on ERA5 in the historical period (1985–2014).
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The spatial distribution of precipitation (mm) in the study area based on ERA5 in the historical period (1985–2014).

Figure 2
The spatial distribution of precipitation (mm) in the study area based on ERA5 in the historical period (1985–2014).
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The spatial distribution of precipitation (mm) in the study area based on ERA5 in the historical period (1985–2014).

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To investigate future precipitation changes, first, 11 GCMs from CMIP6 were selected with the precipitation data of 100-km resolution. The specifications of the 11 GCMs are given in Table 1. The precipitation data of the models were downloaded from the ESGF center (https://esgf-node.llnl.gov/search/cmip6/). Then for each meteorological station, the observational and historical precipitation values were extracted using MATLAB codes and were interpolated within the nearest grid data of ERA5 and GCMs in the historical period (1985–2014) and the error rate has been reduced through bias correction. In this study, linear scaling (LS) bias correction method was used for downscaling of precipitation. The LS method aims to perfectly match the long-term monthly mean of corrected values with those of observed values (Lenderink et al. 2007). Precipitation is typically adjusted with a multiplier factor. By definition, corrected GCM–RCM simulations will perfectly agree in their monthly mean values with the observations. The LS method operates with monthly correction values based on the differences between observed control and raw/uncorrected data (Mendez et al. 2020):
(1)
(2)
where P is precipitation, contr is GCM–RCM simulated time series during the control period, obs is the observational time series during the control period, frc is the future forecast time series to be corrected, BC is the final bias-corrected time series, t is the time step, and is the long-term monthly mean.
Table 1

Information of CMIP6 models used in this study, including model name, institutions, and country

RowModelsInstitutionCountry
1 BCC_CSM2_MR Beijing Climate Center, Meteorological Administration China 
2 BCC_ESM1 
3 CanESM5 Canadian Centre for Climate Modelling and Analysis Canada 
4 CESM2_WACCM_FV2 National Science Foundation, Department of Energy, National Center for Atmospheric Research USA 
5 IPSL_CM6A_LR Institute Pierre-Simon Laplace France 
6 MIROC_ES2L JAMSTEC, AORI, NIES, and R-CCS Japan 
7 MIROC6 
8 MPI_ESM1_2_HR Max Planck Institute for Meteorology (MPI-M) Germany 
9 MRI_ESM2_0 Meteorological Research Institute Japan 
10 NESM3 Nanjing University China 
11 NorESM2_MM Norwegian Climate Centre Norway 
RowModelsInstitutionCountry
1 BCC_CSM2_MR Beijing Climate Center, Meteorological Administration China 
2 BCC_ESM1 
3 CanESM5 Canadian Centre for Climate Modelling and Analysis Canada 
4 CESM2_WACCM_FV2 National Science Foundation, Department of Energy, National Center for Atmospheric Research USA 
5 IPSL_CM6A_LR Institute Pierre-Simon Laplace France 
6 MIROC_ES2L JAMSTEC, AORI, NIES, and R-CCS Japan 
7 MIROC6 
8 MPI_ESM1_2_HR Max Planck Institute for Meteorology (MPI-M) Germany 
9 MRI_ESM2_0 Meteorological Research Institute Japan 
10 NESM3 Nanjing University China 
11 NorESM2_MM Norwegian Climate Centre Norway 
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The historical data of the CMIP6 models are up to 2014, and for comparison, the data up to 2014 should be used, and from 2015 to 2100, it is dedicated to projection of the models. The differences between observational and historical precipitation values were evaluated by the Taylor diagram, the coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) indices. R2 is a dimensionless criterion and its best value is 1, Equation (3) shows how it is calculated (Goudarzi et al. 2015; Houshyar et al. 2018). MSE can vary from 0 to infinity in excellent performance, which is defined in the form of Equation (4) (Goudarzi et al. 2017). RMSE is used as an index for showing the difference between the simulated values and the measured values. This criterion, which is defined as Equation (5), is used as the most common error index (Salahi et al. 2017). MAE is also utilized for comparing case-to-case relative error of simulated values based on the measured values, which is defined in Equation (6) (Shiravand & Hosseini 2020).
(3)
(4)
(5)
(6)

In the above equations, indicates observed data, represents simulated data, and N shows the count of data.

The Taylor diagram evaluates the overall skill of the models in reproducing the spatial pattern of precipitation. The Taylor diagram calculates a summary of the correlation coefficient, the RMSE, and the ratio of spatial standard deviation (RSD). This diagram determines the similarity between the observations and simulations using their correlation and variability ratio (Taylor 2001; Wang et al. 2018).

After evaluating the 11 GCMs, the best model was selected. Based on the best GCM, the precipitation changes were projected under three SSPs in the future period (2050–2021). Finally, those changes were compared to the historical period (1985–2014). Scenarios studied in this study, i.e., SSP1-2.6, SSP3-7.0, and SSP5-8.5 represent the low end of the range of future forcing pathways, medium to high end of the range of future forcing pathways, and high end of the range of future pathways, respectively (O'Neill et al. 2016). Figure 3 shows the flowchart of methodology in this study.
Figure 3
Flowchart of methodology in this study.
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Flowchart of methodology in this study.

Figure 3
Flowchart of methodology in this study.
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Flowchart of methodology in this study.

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In this study, 11 GCMs were investigated. To assess the models’ performances, the observational and historical precipitation values were analyzed in the historical period (1985–2014). The results of evaluating different models using the R2 index (as a spatial distribution) are shown in Figure 4. The results showed that the performance accuracy of most of the models in lower precipitation areas was less than other areas in the region. On the other hand, heavy precipitation, topography, and different atmospheric systems were the most important reasons for the increase in error in some climatic zones of the study area. The ranking results of 11 GCMs’ performances for precipitation simulation in the MENAP region are given in Table 2. In general, based on the R2 values, the MPI-ESM1-2-HR model had higher accuracy in precipitation simulation in the region. The mean R2 and RMSE of the MPI-ESM1-2-HR model compared with ERA5 precipitation data were calculated by 0.6 and 18.9, respectively, in the study area. Accordingly, the best models were determined as the MPI-ESM1-2-HR model, followed by IPSL-CM6A-LR and BCC-CSM2-MR (the CMIP6 models) with the R2 of 0.52 and 0.51, respectively. Besides, the NorESM2-MM and MIROC-ES2L models were ranked by average. The CanESM5 model with the R2 value of 0.36 had the weakest performance among the 11 models for precipitation simulation in the study area.
Table 2

Assessment of CMIP6 model's performance in the simulation of precipitation over the MENAP region based on different indexes

RowModelMAEMSERMSER2
1 MPI-ESM1-2-HR 14.22 705.07 18.88 0.60 
2 IPSL-CM6A-LR 16.92 1,034.05 21.79 0.52 
3 BCC-CSM2-MR 13.54 628.49 17.68 0.51 
4 NorESM2-MM 12.81 533.15 16.98 0.49 
5 MIROC-ES2L 16.6 958.40 21.7 0.47 
6 MIROC6 14.81 718.84 20.07 0.46 
7 MRI-ESM2-0 14.75 755.1 19.63 0.46 
8 NESM3 15.69 813.86 20.78 0.45 
9 BCC-ESM1 17.32 1,054.51 22.46 0.42 
10 CESM2-WACCM-FV2 16.05 795.62 20.67 0.41 
11 CanESM5 17.20 1,060.39 22.72 0.36 
RowModelMAEMSERMSER2
1 MPI-ESM1-2-HR 14.22 705.07 18.88 0.60 
2 IPSL-CM6A-LR 16.92 1,034.05 21.79 0.52 
3 BCC-CSM2-MR 13.54 628.49 17.68 0.51 
4 NorESM2-MM 12.81 533.15 16.98 0.49 
5 MIROC-ES2L 16.6 958.40 21.7 0.47 
6 MIROC6 14.81 718.84 20.07 0.46 
7 MRI-ESM2-0 14.75 755.1 19.63 0.46 
8 NESM3 15.69 813.86 20.78 0.45 
9 BCC-ESM1 17.32 1,054.51 22.46 0.42 
10 CESM2-WACCM-FV2 16.05 795.62 20.67 0.41 
11 CanESM5 17.20 1,060.39 22.72 0.36 
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Figure 4
Spatial distribution of the R2 between historical data of GCMs from CMIP6 and observation (ERA5) for precipitation over the MENAP region in the historical period (1985–2015).
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Spatial distribution of the R2 between historical data of GCMs from CMIP6 and observation (ERA5) for precipitation over the MENAP region in the historical period (1985–2015).

Figure 4
Spatial distribution of the R2 between historical data of GCMs from CMIP6 and observation (ERA5) for precipitation over the MENAP region in the historical period (1985–2015).
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Spatial distribution of the R2 between historical data of GCMs from CMIP6 and observation (ERA5) for precipitation over the MENAP region in the historical period (1985–2015).

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According to the RMSE index, the NorESM2-MM model had mainly the best performance, while underestimation and overestimation were observed in some areas. Based on this index, the CanESM5 model with a RMSE of 22.7 had the weakest performance. Our results were in agreement by Qin et al. (2021), You et al. (2021), Richter & Tokinaga (2020), Babaousmail et al. (2021), Yue et al. (2021) and Majdi et al. (2022) that obtained the high accuracy of CMIP6 models in temperature and precipitation simulations. The highest RMSE of the CMIP6 models were related to the highlands, especially in the east part of the study area, which might be related to the complex physical characteristics of those areas as well as the low ability of climate models to specify the higher altitude and mountainous areas (Gao et al. 2008). What is given in Table 2 is the result of ranking and categorization based on different indicators.

To further evaluate the general skills of the models in the reanalysis of the spatial precipitation patterns, the Taylor diagram was also used. Figure 5 shows the Taylor diagram for the 11 CMIP6 models. Based on the results, the R2 values of the models were calculated from 0.36 to 0.6 in precipitation simulation in the study area. The MPI-ESM1-2-HR model exhibited the highest capability, and the CanESM5 model showed the weakest performance. In general, based on the performance evaluation's results of CMIP6 models, the MPI-ESM1-2-HR model's robustness was confirmed for precipitation projection in the MENAP region, and this model had the lowest variability compared to ERA5 data in the historical period (1985–2014). The results were consistent with a study by Hamadalnel et al. (2021) on the accuracy of the MPI-ESM1-2-HR model in simulating monsoon precipitation in Sudan.
Figure 5
Performance of CMIP6 models based on the Taylor diagram in simulating of precipitation over the MENAP region.
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Performance of CMIP6 models based on the Taylor diagram in simulating of precipitation over the MENAP region.

Figure 5
Performance of CMIP6 models based on the Taylor diagram in simulating of precipitation over the MENAP region.
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Performance of CMIP6 models based on the Taylor diagram in simulating of precipitation over the MENAP region.

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After defining the best model (MPI-ESM1-2-HR) with the best performance in precipitation simulation, precipitation projection was performed using the model in the future period (2021–2050) based on the three SSP scenarios. According to the SSP1-2.6 scenario, the precipitation will range between 0.9 and 1,463.5 mm (Figure 6(a)), and it will vary from −75 to +47 mm compared to the historical period in the study area. The increasing changes were related to parts of northern Turkey, northeastern Pakistan, and southwestern Saudi Arabia (between 5 and 47 mm in the future compared to the historical period), which might be the result of adjacency to water resources such as the Black Sea and the Red Sea as well as the regional topography. In other parts of the study areas, the precipitation will decrease, mainly in the southwestern parts of the Caspian Sea, Pakistan, and southern Sudan. In essence, those regions are among the highest precipitation regions (within the study area) and have a large amount of reduction relative to the amount of precipitation than other areas. However, in general, the amount of precipitation will decrease between 5 and 21 mm in the historical period in most of the areas, and most of the change levels related to the precipitation decrease (Figure 6(b)).
Figure 6
The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP1-2.6 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.
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The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP1-2.6 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.

Figure 6
The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP1-2.6 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.
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The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP1-2.6 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.

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Based on the SSP3-7.0 scenario, the spatial distribution of precipitation values showed that the precipitation will be between 1 and 1,387.6 mm (Figure 7(a)) in the study area, while those values will fluctuate between −75.2 and +43 mm compared to the historical period. The increasing changes (between 3 and 43 mm) were observed in southern parts of the Black Sea, southeast of the Red Sea, and parts of northeastern Pakistan. In other parts of the study area, the precipitation will decrease, mostly in the northeastern regions of the case countries such as Iran, Iraq, Syria, and parts of Pakistan and Afghanistan. Generally, despite the changes in some areas, the precipitation amount will decrease by moving from west to east of the study area (Figure 7(b)).
Figure 7
The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP3-7.0 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.
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The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP3-7.0 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.

Figure 7
The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP3-7.0 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.
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The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP3-7.0 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.

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Based on the SSP5-8.5 scenario, the spatial distribution of precipitation values showed that the precipitation will be between 1.3 and 1,748.9 mm (Figure 8(a)) in the study area, while those values will change between −77.3 and +51.1 mm compared to the historical period. Similar to the SSP3-7.0 scenario, increasing changes will happen in parts of the southern Black Sea and southeastern Red Sea (between 8 and 51 mm). But, predominantly, the study area (in parts of Iran, Iraq, Syria, Pakistan, Afghanistan, Morocco, Algeria, and Sudan) will experience a precipitation decrease. In general, the amount of precipitation will decrease between 6 and 77 mm in most of the study areas (Figure 8(b)). The results are consistent with a study by Hamed et al. (2021) on precipitation reduction (by 10–26 mm) based on CMIP6 models in Egypt.
Figure 8
The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP5-8.5 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.
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The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP5-8.5 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.

Figure 8
The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP5-8.5 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.
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The spatial distribution of precipitation (mm) based on the MPI-ESM1-2-HR model under the SSP5-8.5 scenario (a) and its changes (mm) compared to the historical period (b) over the MENAP region.

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In general, considering the results of different scenarios, the precipitation will decrease in most regions, and most of the changes are related to the zones with lower precipitation levels than the historical period. Decreasing changes in some parts of the study areas could also be the result of proximity to water resources, such as the Black Sea, the Red Sea, and the Mediterranean (with higher humidity) or mountainous and high-altitude regions. Our findings were consistent with a previous study by Sarış (2020) regarding the higher susceptibility of the Black Sea and Mediterranean coasts to heavy precipitation and another research by Majdi et al. (2022) on the precipitation reduction (between 5 and 133 mm on average) in most MENA regions based on an average of 23 GCMs. The comparison between different scenarios also revealed that the SSP5-8.5 scenario had more decreasing and increasing changes than other scenarios, while the SSP1-2.6 scenario demonstrated fewer changes due to the scenarios’ characteristics and specifications.

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Projection of precipitation variation is essential to estimate the extent of the future changes for taking all necessary measures and timely mitigation and reduction of the adverse effects of climate change on water resources and agriculture. The importance of precipitation projection is apparent during the extreme events such as flood and drought or when there is a small, or significant, or sudden drop in heavy rainfall at geographical locations. For that reason, 11 GCMs of CMIP6 with 100-km resolution (in the historical period 1985–2014) were used and evaluated for precipitation simulation and variations projection under three SSPs in the MENAP region. Then, the future period (2021–2050) under different scenarios was compared to the historical period. The results of GCMs evaluation against different indices showed that the MPI-ESM1-2-HR model was more accurate than other models to simulate precipitation in most parts of the study area. The results of precipitation changes projection also revealed that precipitation will increase in some areas such as the coastal areas of the Black Sea, the Mediterranean Sea, and the Red Sea, as well as mountainous and higher altitude areas in the east of the study area and will decrease in other areas. Regarding the historical period, maximum (increase and decrease) changes were estimated by the SSP5-8.5 scenario by −77.3 to +51.1 mm. However, the precipitation will decline in most areas, in the future period (compared to the historical period), with the maximum decrease in the northeastern and eastern parts of the study area and the Middle East countries. The results of this study can assist the decision-makers in dealing with hazardous situations such as drought, flood, environmental degradation, and managing water resources, agriculture, energy, etc. The finding of this research can be adopted by national and international agencies for climate change monitoring, sustainable mitigation, and development.

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The authors of the present paper are grateful to Earth System Grid Federation (ESGF) and European Centre for Medium-Range Weather Forecasts (ECMWF) for providing the data needed to conduct this research.

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The authors received no financial support for the research, authorship, and/or publication of this article.

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The data used in this paper have been prepared by referring to Earth System Grid Federation (ESGF) from this link: https://esgf-node.llnl.gov/search/cmip6/.

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In this paper, custom code in MATLAB software has been used for the evaluation/performance of GCMs and extraction of dataset in the study area.

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E.M. and S.A.H. conceived the presented idea and developed the theory and performed the computations. M.S.H. and M.H. verified the analytical methods and supervised the findings of this work. L.G.P. encouraged and developed the theoretical formalism. All authors discussed the results and contributed to the final manuscript.

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Not applicable, because this article does not contain any studies with human or animal subjects.

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The data of this research were not prepared through a questionnaire.

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There is no conflict of interest regarding the publication of this article. The authors of the article make sure that everyone agrees to submit the article and is aware of the submission

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All relevant data are included in the paper or its Supplementary Information.

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The authors declare there is no conflict.

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