Evaluation and Projection of CMIP6 Simulations of Climate Variables in the Rift Valley Lakes Basin, Ethiopia

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Comprehensive rating metric (CRM) based on seven statistical metrics was used to evaluate the performance of GCMs. The multi-model mean ensemble (MMME) of four carefully selected best performing CMIP6-GCMs for each climate variables were used for projection. Considering respective MMMEs, the projected mean precipitation, maximum temperature (Tmax), minimum temperature (Tmin), and relative humidity (hurs), will increase, but solar radiation (rsds) will decline, under all SSPs in both periods as response to global warming. The projected precipitation increase may augment water availability in the Rift valley Lakes Basin (RVLB). However, more intense and frequent heavy precipitation with short-duration may lead to flash floods and landslides to damage crops and infrastructures. In addition, raise on Tmax, Tmin and windspeed may lead to high evapotranspiration demand, recurrent drought, and water insecurity. To properly comprehend and respond appropriately, more research is needed to determine how these changes in climate variables affect sustainable water resources management and water security in RVLB. Bias correction climate variables CMIP6 Comprehensive rating metrics GCM RVLB Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction The anthropogenic climate change is already affecting the human and natural systems across the globe through its contributions to the observed changes in severity and frequency of weather and climate extremes (Eyring et al 2021 ; IPCC 2023 ). Climate change has been a serious challenge for vulnerable regions due to their high exposure and low adaptive capacity such as in Africa (Niang et al 2014 ; Trisos et al 2022 ). East Africa is one of the hotspots of high human vulnerability to climatic hazards, such as drought and flooding, among the regions projected to be susceptible to climate change (Trisos et al 2022 ; WMO 2023 ). Furthermore, Ethiopia is an extreme example of being impacted by climate change due to its dependence on rainfed agriculture (Conway and Scipper 2011; WB 2021), and worst droughts in 40 years (WMO 2023 ). Predictably, climate change will amplify existing stress on water availability, and exacerbate the vulnerability of agricultural systems and land degradation (Niang et al 2014 ; Taye et al 2018 ; Trisos et al 2022 ; IPCC 2023 ). General circulation models (GCMs) have been commonly used for investigating the response of the climate system, and have been applied for an understanding of past, present, and future climate variability and change (Stouffer et al 2017 ; Eyring et al 2021 ). The Coupled Model Intercomparison Project (CMIP) has provided remarkable contributions for simulation and projection of climate change via spatiotemporal variability of climate variables (Eyring et al 2016 ). The CMIP of phase sixth (CMIP6) has been designed to produce multi-model datasets to advance knowledge of climate variability and climate change (Eyring et al 2016 ). CMIP6 models have been developed based on CMIP5 models to address major scientific gaps identified (Stouffer et al 2017 ), and to provide a set of state-of-the-art GCM simulations to support the IPCC Sixth Assessment Report (Eyring et al 2016 ; IPCC 2021). Moreover, it is reported that the climate models participating in CMIP6 have shown improvements in their models’ ability to simulate past and present climate in terms of physical parameterizations, model performance, higher horizontal resolution, reproducing climate, and internal variability compared to CMIP5 predecessors (Eyring et al 2016 , 2021 ). However, the evaluation of CMIP6 GCMs in different regions indicated region specific performance in simulating climate variables. CMIP6 GCMs have been used to investigate the mean temperature and precipitation (e.g., Almazroui et al 2020 ; Alaminie et al 2021 ; Gebresellase et al 2022 ; Rettie et al 2023 ; Gashaw et al 2024 ), surface temperature (Fan et al 2020 ; Alaminie et al 2021 ; Ayugi et al 2021b ), wind speed (Akinsanola et al 2021; Zha et al 2023 ), relative humidity and solar radiation (Li et al 2021 ; Song et al 2023 ), and estimation of evapotranspiration (Song et al 2023 ). Similarly, the GCMs from CMIP6 have been applied to assess climate change and variability in different parts of Ethiopia. For instance, Alaminie et al ( 2021 ) identified BCC-CSM2-MR and MRI-ESM2-0 as best performing for mean precipitation and maximum temperature from 21 CMIP6 GCMs, respectively and they projected slightly increasing precipitation and warming trend over the Blue Nile basin under four SSPs. Another study in Awash basin by Gebresellase et al ( 2022 ) also selected five CMIP6-GCMs as best performers for the mean climate, and they found an increasing future temperature in all parts of the Awash basin. The studies showed that the CMIP6 GCMs could accurately simulate precipitation and temperatures even though there is considerable variation among regions, seasons, and climate variables. Although robust improvements are reported by researchers, the performance of GCMs shows inconsistency, bias, and discrepancies in simulating observed climate variables from region to region. As CMIP6 provides valuable insights into the potential impacts of climate change, it is essential to understand the performance of CMIP6-GCMs, and how well these are used for future projections will be critical to sustainable natural resources management, and climate change adaptation planning and future projection over RVLB. In addition, the above-mentioned studies focused on precipitation and temperature without considering the under-studied relative humidity, solar radiation and windspeed which are large source of uncertainty in global land surface modeling (Li et al 2021 ), and significant role in hydrologic cycle, agricultural production, renewable energy and other applications (Wu et al 2020 ; Akinsanola et al 2021; Dieng et al 2022 ; Zha et al 2023 ). GCM outputs exhibit substantial systematic modeling errors with differences between simulated and observed climate statistics (Chen et al 2013 ; Maraun 2016 ; Cannon 2018 ). To overcome the problems of biases in GCMs to variety of contexts of applications, bias-correction is necessary to act as an interface between simulations from climate models and impact modeling (Chen et al 2013 ). In hydro-climatic impact studies, numerous approaches of bias correction techniques have been employed (e.g., Gudmundsson et al 2012 ; Teutschbein and Seibert 2012 ; Chen et al 2013 ; Cannon 2015, 2018 ). However, quantile mapping (QM) outperforms other bias correction methods that only adjust the variance or mean due to its ability preserve the statistical properties of observations at all quantiles and representing the entire marginal distribution of observed variables (Chen et al 2013 ; Gudmundsson et al 2012 ). QM has been extensively used to adjust the biases in GCMs (Ayugi et al 2021b ; Dieng et al 2022 ; Meresa et al 2022 ; Rettie et al 2023 ; Gashaw et al 2024 ). As in many parts of Ethiopia, the economy of the inhabitants of the RVLB is heavily dependent on natural resources, and on rain-fed agriculture which is significantly affected by the adverse impacts of climate change (Conway and Schipper 2011 ; Wagesho et al 2012 ; Ayalew et al 2022 ). It is particularly vulnerable to the effects of a warming and drying climate, which could adversely impact crop yields and pastures. The basin is a densely populated area and prone to high climate variability, frequent drought and flash flood events (Viste et al 2013 ; Tesfamariam et al 2019 ). For sustainable adaptation and mitigation planning, CMIP6 could provide valuable insights into the potential impacts of climate change in RVLB. In such a vulnerable basin to climate variability and change, studies on the multi-model CMIP6-GCM evaluations and projections are scarce, making it difficult for policymakers and end users to get up-to-date information. In addition, this is the first initial study using CMIP6 to simulate and project the climate variables except studies in Ethiopia as whole by Birhanu et al (2023) on model evaluation, Rettie et al ( 2023 ) on evaluation and projection of precipitation and temperature, and bias uncorrected GCMs in Abaya-Chamo subbasin (Ersado and Awoke 2024 ). This implies the ability of GCMs to reproduce the historical and future climate of RVLB has not been widely studied. Therefore, it is crucial to evaluate the performance of the GCMs to confirm the extent to which they can reproduce the observed climate from multi-GCM ensembles. Hence, this study provides essential information for effective climate change adaptation and mitigation strategies to cope up from devastating impacts of climate related hazards through sustainable natural resources management. The objective of this study is to evaluate the performance of GCMs and project changes in six climate variables (precipitation, Tmax, Tmin, relative humidity, solar radiation and windspeed) using CMIP6 in RVLB for the reference period (1985–2014), and the two future scenarios of 2050s (2035–2064) and 2080s (2065–2094) under SSP2-4.5, SSP3-7.0 and SSP5-8.5. This article is organized as follows. Section 2 explains the study area, models, data and methods used in this study. Section 3 presents evaluation results and projection of CMIP6 models with respect to climate variables in RVLB. A conclusion is provided in Section 4. 2 Materials and Methods 2.1 Description of the study area The Rift Valley Lakes Basin (RVLB) is located in the southern part of the Main Ethiopian Rift with an area of about 53,000 km². Its elevation ranges from 450m over the southern pastoral area of the rift valley to 4,190 m in the northeast highland (Fig. 1). The basin provides fresh water supply and diverse ecosystems benefits for more than 15 million people who mostly depend on subsistence agriculture. The basin is densely populated with more than 200 people per square kilometer and prone to high climate variability, frequent drought and flood events (Viste et al 2013 ; EPCC 2015; Tesfamariam et al 2019 ). Intensive subsistence small holder rainfed crop production (annual and perennial) and livestock rearing are the decisive backbones of the livelihood. Among the food and cash crops, coffee and enset have economically significant in most parts of the RVLB (Wagesho et al 2012 ; Tesfamariam et al 2019 ). Crops like also cereals including maize teff, wheat barley, vegetables, and fruits are commonly grown (Ayalew et al 2022 ). The basin is characterized by a bi-modal precipitation pattern during FMAM (February to May, short rainy season, also locally known as ‘Belg’) and JJAS (June to September, also locally known as ‘Kiremt’) in highland. The southern part receives its major precipitation during FMAM and SON (September, October and November). The RVLB receives mean annual precipitation of 849 mm with 1021.2 (± 94.8) mm, 841.2 (± 85.6) mm, and 670 (± 86.9) mm over highlands, midlands and lowlands, respectively for period 1985–2014. The mean annual Tmax in the RVLB ranged from 32.99°C in the lowland to 19.4°C in the highlands. The mean Tmin ranged from 17.47°C in the lowland to 7.58°C in the highlands. Insert Fig. 1 2.2 Climate Data The performance of CMIP6 models strongly depends on the availability and reliability of the reference data sets (Li et al 2021 ). The daily gridded precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) product of Ethiopia known as Enhancing NAtional ClimaTe Services (ENACTS) used as daily observed for bias correction and evaluation of the skill of GCMs were obtained from the Ethiopian National Meteorological Institute (NMI) ( www.ethiomet.gov.et/ ). These data have grid of 0.0375° x 0.0375°. Since actual ground-based wind speed dataset over RVLB are sparse and not publicly available, daily near surface wind speed data (sfcWind) was obtained from the European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis version 5 (ERA5) atmospheric reanalysis product (Hersbach et al 2020 ). Similarly, Near Surface Relative Humidity (hurs), and Surface Downwelling Shortwave Radiation (rsds) data were obtained from EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI) dataset product for bias-correction (Lange 2019 ) Daily precipitation, Tmax (tasmax), Tmin (tasmin), hurs, rsds and sfcWind data outputs from 28 CMIP6 (Table 1 ) models were downloaded from ESGF Metagrid ( https://aims2.llnl.gov/search/ ) of the World Climate Research Program (WCRP). The models including the climate variables under both historical CMIP simulations and a corresponding future ScenariMIP (Eyring et al 2016 ; O’Neil et al 2016) with daily frequency was considered. For the purpose of comparison, GCMs and observed data were re-gridded to a common grid of 0.25° × 0.25° horizontal resolution by using the popular bilinear interpolation method (Amazroui et al 2020; Li et al 2021 ; Rettie et al 2023 ). In addition, to perform a consistent analysis of the historical and the future scenarios, first member realization outputs (r1i1p1f1/f2) and the Tier-1 (highest priority) scenarios shared socio-economic pathways (SSPs) including SSP2-4.5, SSP3-7.0 and SSP5-8.5 were selected for evaluation and projection (O’Neill et al 2016 ; Meinshausen et al 2020 ). The future climate was projected for 2035–2064 (2050s) and 2065–2094 (2080s) in comparison to the baseline period (1985–2014). Table 1 List of the GCMs from the CMIP6 used in this study Model Institution Country Resolution (km) ACCESS-CM2 CSIRO Australia 250 ACCESS-ESM1.5 CSIRO Australia 250 BCC-CSM2-MR Beijing Climate Center China 100 CESM2 NCAR USA 100 CMCC-CM2-SR5 CMCC Italy 100 CMCC-ESM2 CMCC Italy 100 100 CanESM5 CCma Canada 500 CNRM-CM6‐1 CNRM-CERFACS France 250 CNRM-CM6‐HR CNRM-CERFACS France 50 CNRM-ESM2‐1 CNRM-CERFACS France 250 EC-Earth3 EC-Earth Consortium Europe 100 EC-Earth3-Veg EC-Earth Consortium Europe 100 FGOALS-g3 Chinese Academy of Sciences China 250 GFDL-CM4 GFDL USA 100 GFDL-ESM4 GFDL USA 100 GISS-E2-1-G NASA- GISS USA 250 INM-CM4-8 INM Russia 100 INM-CM5-0 INM Russia 100 IPSL-CM6A-LR Institut Pierre-Simon Laplace France 250 MIROC6 MIROC Japan 250 MPI-ESM1-2-HR Max Planck Institute Germany 100 MPI-ESM1-2-LR Max Planck Institute Germany 250 MRI-ESM2-0 Meteorological Research Institute Japan 100 NESM3 NUIST China 100 NorESM2-LM Norwegian Climate Centre (NCC) Norway 250 NorESM2-MM NCC Norway 100 TaiESM1 AS-RCEC Taiwan 100 UKESM1-0-LL Met Office Hadley Centre (MOHC) UK 250 Insert Table 1 2.3 Model bias correction Bias correction is an essential post-processing procedure that helps to minimize the inherent systematic error in GCMs, and improve the quality of the simulated outputs. The quantile mapping (QM) bias correction technique was applied to adjust the distribution of daily raw output of climate variables with the distribution of daily observed climate variables at each grids using quantile mapping packages on R statistical software ( https://www.r-project.org/ ). Multivariate bias correction that matches the multivariate distribution using quintile delta mapping (QDM) and the N-dimensional probability density function transform (N-pdft) (MBCn) was used to adjust the bias of precipitation, Tmax, Tmin, hurs, rsds and sfcWind (Cannon et al 2015 , 2018, 2023). MBCn transforms all statistical characteristics of the observed distribution to GCM outputs. In addition, QDM has a widely desired feature for studies examining the impacts of climate change through preserving the trends of projections in all quantiles and transforming all features of the observed data distribution to the equivalent distribution from GCM (Maraun 2016 ; Cannon 2018 , 2023 ; Dieng et al 2022 ; Marchard et al 2024). 2.4 Model performance metrics The historical experiments of 28 GCMs were compared with the observed/reanalysis historical climatic variables throughout 1985–2014 to select the best performing models for each variable using statistical metrics. The Mean Error (ME), Mean Absolute Error (MAE), the Root Mean Squared Error (RMSE), Ratio of the RMSE to the standard deviation of the observations (RSR), percent bias (PBIAS), index of agreement (d), Pearson correlation coefficient (r), and Kling-Gupta Efficiency (KGE) metrics were computed to evaluate the GCMs performance in order to select skillful models in simulating the near-surface climatic variables. The metrics measure the average error magnitude between the simulated and the observed monthly timeseries. The smaller the ME, MAE, PBIAS, RSR, and RMSE the better the performance of the models. Whereas the higher the d, r and KGE close to an optimal value of 1, the models are better performing. These metrics were used to evaluate the performance of CMIP6-GCMs (e.g., Alaminie et al 2021 ; Ayugi et al 2021b ; Berhanu et al 2023 ; Rettie et al 2023 ). Simulated monthly timeseries were evaluated and compared against the observed counterparts using ME, MAE, RMSE, RSR, PBIAS, d, r and KGE. These metrics were estimated using hydroGOF r package (Mauricio 2024 ) To understand and quantify the relative performance of each GCMs, the overall ranking of the GCMs simulation performance was conducted by combining the above metrics obtained with a comprehensive rating metrics (CRM) (Birhanu et al 2023; Gashaw et al 2024 ), which is defined as: $$\:\text{C}\text{R}\text{M}=1-\frac{1}{\text{n}\text{m}}\sum\:_{\text{i}=1}^{\text{n}}{\text{r}\text{a}\text{n}\text{k}}_{\text{i}}$$ 1 where m is the number of models, and n is the number of metrics. The rank varies from 1 for the best-performing model to 28 for the worst model for each metrics. Therefore, the closer CRM is to 1, the better the model performs. Based on the CRM values, ensemble of multi-model mean ensemble (MMME)of top four best performing bias corrected GCMs were used for projection. 2.5 Projected change analysis Based on historical and future climate datasets from CMIP6-GCMs, projected changes in climate variables under the Tier-1 Scenario Model Intercomparison Project (ScenarioMIP) were investigated as described in detail in (e.g., O’Neill et al 2016 ; Meinshausen et al ( 2020 ). These scenarios include SSP2-4.5, SSP3-7.0 and SSP5-8.5. A grid point based analysis of the projected changes in climate variables were expressed by the relative percentage changes and absolute change, between future scenario (2035–2064 and 2065–2094) to the historical baseline of 1985–2014 (Taye et al 2018 ). To efficiently minimize the significant uncertainty from individual GCMs, the MMME of the most performing GCMs for each variable was used to project the future climate (Flato et al 2013 ; Gashaw et al 2024 ). Ordinary kriging on ARCGIS 10.8 was used for interpolating the grid point based PBIAS, mean and change values to illustrate the spatial distribution and variation over RVLB. 3 Results and Discussion 3.1 Bias correction of the GCMs To provide an adequate support for informed decision on climate change adaptation, rigorous local scale climate change projections at high spatial and temporal resolutions are required. Fig. S1 shows the spatial patterns of the bias between the simulated mean annual precipitation and observations for the period 1985–2014. The result shows a considerable bias and inconsistency of raw GCMs in simulating precipitation over the RVLB. Majority of the raw GCMs showed overestimation of precipitation over North of RVLB (for example, MIROC6 by PBIAS = 278%)), and across Southern by INM-CM4-8 (PBIAS = 278%), whereas underestimation (for instance, CNRM-CM6-1-HR (PBIAS= -80%), CNRM-ESM2-1 (PBIAS= -77%), and CNRM-CM6-1 (PBIAS= -76%) around southern and southwest part of RVLB (Fig. S1 ). Higher biases were also reported by Rettie et al ( 2023 ) from MIROC6 model for a considerable part of the Ethiopia. The larger bias from the models requests the need for bias correction (Cannon 2018 ; Song et al 2023 ; Machard et al 2024 )). In this regard, the observed data appears to have been more accurately represented by the bias corrected GCMs (Fig. 2a, 3a, 3b). MBCn has improved the performance and confirmed from simulated precipitation of MMME. The spatial distribution of bias adjusted historical precipitation (Fig. 3a, 3b) closely match the observed by reducing the systematic model biases (Table 2 ). As depicted on Fig. 3a, 3b and Table 2 , it is evident that the bias corrected GCMs are closer to the observed than the uncorrected (raw) GCMs, although not being exactly the same as the observed for all GCMs (e.g., CMCC-ESM2, PBIAS = 17.8%). Therefore, it is crucial to evaluate the performance of bias corrected GCMs in simulating the precipitation in order to select subset of best performing GCMs to estimate the multi-model mean ensemble (MMME). Table 2 Comprehensive rating metrics (CRM) of bias corrected GCMs outputs rank based on ME, MAE (mm), RMSE (mm), PBIAS (%), RSR, d, r and KGE of historical monthly precipitation of CMIP6 simulations as compared to the observed precipitation for period 1985–2014 over RVLB. GCMs ME MAE RMSE PBIAS RSR d r KGE CRM MMME −1.3 27.3 35.4 −0.3 0.78 0.82 0.68 0.68 0.94 MME27 −4.1 26.2 34.2 −5.4 0.75 0.81 0.68 0.62 0.90 GFDL-CM4 −1.7 33.9 44.1 −2.2 0.97 0.76 0.58 0.57 0.85 BCC-CSM2-MR −2.6 32.8 43.6 −3.5 0.96 0.75 0.56 0.55 0.85 MPI-ESM-2-HR −3.4 33.9 43.5 −4.5 0.96 0.75 0.59 0.53 0.84 GFDL-ESM4 0.80 34.4 46.1 1.0 1.02 0.75 0.56 0.54 0.81 MIROC6 −1.8 35.1 46.8 −2.4 1.03 0.73 0.54 0.53 0.73 CMCC-ESM2 −13.8 34.8 45.3 −17.8 1.0 0.75 0.59 0.54 0.73 CanESM5 −4.8 32.4 42.8 −6.4 1.03 0.73 0.52 0.51 0.73 FGOALS-g3 −1.4 39.0 50.2 −1.9 1.11 0.75 0.59 0.46 0.69 CMCC-CM2-SR −4.5 34.6 45.6 −6.0 1.0 0.71 0.50 0.50 0.67 NorESM2-MM 0.13 37.9 50.6 0.2 1.11 0.72 0.54 0.46 0.66 MPI-ESM-2-LR −0.2 39.4 52.2 −0.2 1.15 0.72 0.54 0.44 0.58 NESM3 −4.3 36.4 47.1 −5.7 1.04 0.68 0.45 0.45 0.57 TaiESM1 −11.7 37.6 49.0 −15.6 1.08 0.69 0.48 0.46 0.54 INM-CM4-8 −4.9 37.0 50.8 −6.5 1.12 0.69 0.48 0.45 0.52 EC-Earth3 −6.5 37.6 50.3 −8.6 1.11 0.69 0.46 0.44 0.51 CNRM-CM6-HR −8.0 36.6 49.1 −10.6 1.08 0.68 0.44 0.43 0.49 INM-CM5-0 −7.3 37.8 52.0 −9.6 1.14 0.71 0.52 0.44 0.48 ACCESS-CM2 −8.0 40.5 53.2 −10.7 1.17 0.69 0.48 0.42 0.39 CNRM-CM6-1 −0.4 41.8 57.7 −0.6 1.27 0.66 0.44 0.34 0.38 EC-Earth3-Veg −3.8 41.3 53.9 −5 1.19 0.67 0.44 0.4 0.38 MRI-ESM2-0 −4.0 39.3 51.2 −5.3 1.13 0.6 0.31 0.31 0.37 CESM2 −4.4 40.6 51.1 −5.9 1.13 0.59 0.29 0.28 0.33 NorESM2-LM 3.8 45.2 61.8 5.1 1.36 0.65 0.46 0.27 0.31 CNRM-ESM2-1 −0.3 44.9 62.0 −0.4 1.37 0.61 0.38 0.27 0.31 IPSL-CM6A-LR −6.7 40.9 58.5 −8.9 1.29 0.64 0.41 0.33 0.28 UKESM1-0-LL −4.8 45.9 59.0 −6.4 1.3 0.54 0.2 0.19 0.2 ACCESS-ESM1 −6.4 47.6 65.0 −8.5 1.43 0.6 0.37 0.22 0.19 The differences in the Tmax and Tmin between the raw GCMs outputs and observed shows considerable bias ranging from − 32.2% for (e.g., from NESM3 and IPSL-CM6A-LR) to 44% (e.g., from CanESM5 and MIROC6) for Tmax (Fig.S2), and − 7.6% from CNRM-CM6-1 to 131% from NESM3 and NorESM2-LR for Tmin (Fig S3). The result shows the GCMs significantly overestimated Tmin particularly across the northern RVLB with large variability among GCMs and spatial pattern. However, after bias correction using MBCn, all GCMs show a very similar simulation of the observed Tmax and Tmin in terms of desirable PBIAS (~ 0) and ME (~ 0) values (Table 3 ). Table 3 Comprehensive rating metrics (CRM) of bias corrected GCMs outputs rank based on ME, MAE, RMSE, PBIAS (%), RSR, d, r and KGE of historical monthly Tmax, and minimum temperature (Tmin) CMIP6 simulations for period (1985–2014) over RVLB. ME and PBIAS are excluded from this table due desirable values (~ 0) CMIP6 GCMs Tmax Tmin GCMs MAE RMSE RSR d r KGE CRM MAE RMSE RSR d r KGE CRM MMME 0.62 0.81 0.61 0.89 0.80 0.77 0.97 0.40 0.51 0.72 0.81 0.70 0.62 0.96 MME27 0.68 0.86 0.65 0.86 0.76 0.70 0.90 0.39 0.51 0.72 0.8 0.69 0.56 0.90 BCC-CSM2-MR 0.69 0.87 0.66 0.87 0.77 0.75 0.90 0.47 0.60 0.84 0.77 0.60 0.58 0.85 FGOALS-g3 0.77 0.99 0.75 0.84 0.72 0.72 0.77 0.47 0.59 0.83 0.77 0.61 0.59 0.87 NorESM2-MM 0.74 0.93 0.70 0.86 0.74 0.74 0.86 0.45 0.59 0.82 0.75 0.59 0.52 0.75 CMCC-CM2-SR5 0.77 1.00 0.75 0.84 0.71 0.71 0.70 0.49 0.65 0.91 0.76 0.58 0.58 0.62 CNRM-CM6-1 0.88 1.10 0.83 0.79 0.63 0.63 0.41 0.47 0.6 0.85 0.77 0.60 0.59 0.84 TaiESM1 0.78 1.01 0.76 0.84 0.71 0.71 0.67 0.52 0.68 0.95 0.77 0.59 0.58 0.57 MPI-ESM1-2-HR 0.81 1.03 0.78 0.83 0.69 0.69 0.54 0.49 0.64 0.9 0.73 0.55 0.53 0.57 ACCESS-ESM1-5 0.91 1.12 0.85 0.80 0.64 0.64 0.36 0.48 0.64 0.85 0.76 0.62 0.57 0.74 INM-CM5-0 0.75 0.96 0.72 0.85 0.73 0.73 0.82 0.54 0.7 0.98 0.72 0.51 0.51 0.29 CNRM-CM6-1-HR 0.89 1.11 0.84 0.77 0.61 0.6 0.33 0.48 0.63 0.85 0.76 0.60 0.58 0.76 NorESM2-LM 0.81 1.00 0.76 0.83 0.70 0.69 0.61 0.51 0.64 0.89 0.71 0.52 0.47 0.45 EC-Earth3-Veg 0.94 1.22 0.92 0.76 0.57 0.57 0.16 0.47 0.60 0.84 0.78 0.59 0.6 0.85 GFDL-CM4 0.80 1.02 0.77 0.82 0.68 0.67 0.53 0.50 0.65 0.91 0.72 0.54 0.52 0.471 ACCESS-CM2 0.88 1.11 0.84 0.8 0.65 0.65 0.45 0.50 0.64 0.9 0.72 0.53 0.5 0.49 CNRM-ESM2-1 0.94 1.17 0.88 0.75 0.58 0.57 0.20 0.48 0.62 0.87 0.76 0.59 0.58 0.73 CMCC-ESM2 0.80 0.99 0.75 0.84 0.72 0.72 0.74 0.57 0.74 1.04 0.67 0.44 0.44 0.17 INM-CM4-8 0.78 0.99 0.75 0.84 0.72 0.72 0.76 0.60 0.77 1.09 0.65 0.4 0.4 0.10 MPI-ESM1-2-LR 0.78 0.98 0.74 0.83 0.70 0.69 0.68 0.55 0.69 0.97 0.63 0.4 0.35 0.16 GFDL-ESM4 0.90 1.16 0.88 0.77 0.59 0.58 0.25 0.50 0.64 0.9 0.74 0.55 0.54 0.58 EC-Earth3 0.95 1.21 0.92 0.75 0.57 0.57 0.15 0.51 0.64 0.9 0.73 0.55 0.54 0.54 CanESM5 0.89 1.09 0.83 0.80 0.65 0.65 0.46 0.57 0.75 1.05 0.66 0.42 0.42 0.14 GISS-E2-1-G 1.05 1.29 0.98 0.72 0.52 0.52 0.02 0.52 0.67 0.94 0.75 0.57 0.57 0.49 IPSL-CM6A-LR 0.95 1.20 0.91 0.74 0.56 0.56 0.12 0.52 0.69 0.97 0.69 0.47 0.45 0.26 MRI-ESM2-0 0.99 1.27 0.96 0.70 0.48 0.47 0.03 0.52 0.66 0.93 0.71 0.51 0.49 0.35 NESM3 0.89 1.14 0.86 0.78 0.63 0.63 0.35 0.66 0.86 1.2 0.55 0.25 0.25 0.02 MIROC6 1.01 1.24 0.94 0.68 0.47 0.44 0.02 0.54 0.67 0.95 0.71 0.52 0.51 0.34 UKESM1-0-LL 0.90 1.14 0.86 0.78 0.63 0.63 0.31 0.61 0.83 1.16 0.59 0.32 0.32 0.05 The simulations of relative humidity vary by PBIAS=–27.4% from UKESM1-0-LL to PBIAS = 61.1% from CNRM-ESM2-1 (Fig S4) from the monthly EWEMBI data. Furthermore, relatively the raw GCMs simulated the solar radiation with narrow range of PBIAS of − 18.3% from CESM2 to 20.1% from MIROC6 as compared to EWEMBI data (Fig S5). It is consistent with Li et al ( 2021 ) who reported that CMIP6 exhibited the lowest uncertainties and the best performance by simulating net radiation. The underestimation of windspeed ranges from − 73% predominantly from MPI-ESM1-2-LR and FGOALS-g3 to an overestimation of ~ 179% from CESM2, CanESM5, MIROC6 and IPSL-CM6-LR as compared to ERA5 data over RVLB. Majority of the raw GCMs overestimated sfcWind around the Northern RVLB and underestimated over southwestern part of RVLB (Fig S6). The statistical adequacy of the windspeed simulations is less satisfactory compared to ERA5, particularly concerning metrics for raw GCMs. Large biases in the simulation of historical surface wind speed in the current CMIP6 GCMs were also reported (Wu et al 2020 ) over China. 3.2 Climate Models performance in simulating climate variables 3.2.1 Precipitation Table 2 presents a comprehensive rating metric (CRM) for GCMs based on combination of performance scores (ME, MAE, PBIAS, RMSE, RSR, d, r, and KGE) between the simulated and observed values of monthly precipitation. Among all the GCMs, the bias corrected GFDL-CM4, BCC-CSM2-MR, GFDL-ESM4 and MPI-ESM1-2-HR and their ensemble (MMME) outperformed in capturing the monthly precipitation in RVLB (Table 2 ). Whereas, ACCESS-ESM1-5, UKESM1-0-LL and IPSL-CM6-LR showed poor performance to simulate the precipitation over the RVLB. This indicated that MMME provides robust estimates of the precipitation, and it was considerably better than individual GCMs and ensemble of all GCMs (MME27, Table 2 ). The top GCMs compared well with monthly observed precipitation of RVLB and showed better performance across Ethiopia (Berhanu et al 2023 ; Terefe et al 2023; Rettie et al 2023 ; Alaminie et al 2021 ; Ersado and Awoke 2024 ), and East Africa (Ayugi et al 2021b ). Berhanu et al ( 2023 ) evaluated that GFDL-CM4 is the best-performing model followed by GFDL-ESM4, NorESM2-MM, and CESM2 in simulating rainfall over Ethiopia. In addition, Terefe et al (2023) reported GFDL-CM4 is the best-performing over Baro basin. Furthermore, MPI-ESM1-2-HR also revealed as the most performing model in simulating JJAS total precipitation over Ethiopia (Rettie et al 2023 ). Bias corrected BCC-CSM2-MR also showed better performance for simulating the rainfall climatology of the Bale Eco-Region from the daily to annual temporal scales (Gashaw et al 2024 ). Moreover, Ersado and Awoke ( 2024 ) also reported that raw MPI-ESM1-2-HR was among the best performing as compared to CHIRPS data in Abaya-Chamo subbasin. To minimize the uncertainties arising from the weakness of individual models due to systematic errors, there is necessity of using the MMMEs of top performing GCMs in investigating climate simulations and projections (Flato et al 2013 , Ayugi et al 2021a , Gashaw et al 2024 ). Therefore, in this study, the ensemble of the top four GCMs (MMME) was used to the analyze the spatiotemporal changes of precipitation over RVLB. 3.2.2 Temperature Concerning Tmax under monthly performance evaluation based on CRM, BCC-CSM2-MR, NorESM2-MM, INM-CM5-0, and FGOALS-g3 exhibited superior performance by simulating Tmax (Table 3 ). Whereas, MIROC6, GISS-E2-1-G and MRI-ESM2-0 showed poor performance in simulating observed Tmax over RVLB (Table 3 ). This result is consistent with Feyissa et al ( 2023 ) for NorESM2-MM over Omo basin, Terefe et al (2023) for INM-CM5-0 over Baro basin, and (Ayugi et al 2021b ) for FGOALS-g3 over East Africa in reproducing Tmax. In contrast, the performance of MRI-ESM-0 was different from Alaminie et al ( 2021 ) who reported as best performing for temperature over Blue Nile basin, and Rettie et al ( 2023 ) showed larger errors from NorESM2-MM in a larger part of Ethiopia for both Tmax and Tmin. The CMIP6-GCMs were also evaluated in simulating Tmin using CRM. FGOALS-g3, BCC-CSM2-MR, EC-Earth3-Veg and CNRM-CM6-1 exhibited quite consistent agreement with monthly observed Tmin (Table 3 ). From these GCMs, CNRM-CM6-1 and EC-Earth3-Veg outperformed for simulating Tmin and used for future change analysis in Bale Eco-region (Gashaw et al 2024 ). Consequently, the ensemble of the four GCMs was used for future projections and change analysis of Tmin in order to minimize the uncertainty arising from the weakness of individual models and to increase confidence in the projection for decision-making. 3.2.3 Relative humidity, solar radiation and windspeed Evaluating GCMs performance based on variety of climate variables can help to find a more reliable set of GCMs for impact modelling (Eyring et al 2021 ). Among the GCMs, bias corrected CMCC-ESM2, MPI-ESM2-HR, INM-CM5-0 and MPI-ESM2-LR provided the more accurate representation of relative humidity over RVLB (Table 4 ). Similarly, INM-CM5-0, FGOALS-g3, BCC-CSM2-MR, and CMCC-ESM2 are found to have stronger performance for surface downwelling solar radiation (Table 4 ). The MMME of relative humidity (Fig. 2e, 6a, 6b), solar radiation (Fig. 2d, 6a, 6b), and windspeed (Table 4 ; Fig. 2f, 8a, 8b) show overall good agreement in terms of spatial patterns and annual cycles with their respective reference data (Table S1 ). Table 4 Comprehensive rating metrics (CRM) of climate models outputs rank based on mean error (ME), Mean absolute error (MAE), root mean square error (RMSE), ration of RMSE to standard deviation of observed (RSR), degree of agreement (d), Pearson correlation coefficient (r), and Kling-Gupta efficiency (KGE) of historical monthly of historical monthly relative humidity (hurs), near surface dwelling radiation (rsds) and windspeed (sfcWind) CMIP6 simulations for period (1985–2014) over RVLB. ME and PBIAS are excluded from this table due to desirable values (~ 0) GCMs rsds hurs sfcWind MAE RMSE RSR d r KGE CRM MAE RMSE RSR d r KGE CRM CRM MMME4 9.55 13.06 0.62 0.89 0.79 0.63 0.97 5.98 7.55 0.62 0.89 0.80 0.78 0.97 0.96 MME27 10.70 13.71 0.65 0.85 0.76 0.66 0.86 6.01 7.65 0.62 0.87 0.78 0.72 0.91 0.61 ACCESS-CM2 18.32 23.26 1.11 0.63 0.38 0.38 0.10 8.70 11.13 0.91 0.76 0.58 0.58 0.09 0.18 ACCESS-ESM1-5 19.55 23.94 1.14 0.60 0.35 0.35 0.06 8.77 11.09 0.91 0.76 0.59 0.59 0.10 0.18 BCC-CSM2-MR 11.68 15.25 0.72 0.85 0.74 0.74 0.84 7.57 9.95 0.81 0.81 0.67 0.67 0.57 0.90 CanESM5 13.87 18.03 0.86 0.79 0.63 0.63 0.50 7.34 9.75 0.80 0.82 0.68 0.68 0.66 0.13 CESM2 12.80 16.32 0.78 0.83 0.70 0.70 0.69 7.94 10.54 0.86 0.79 0.63 0.63 0.33 0.67 CMCC-CM2-SR5 12.47 16.34 0.78 0.83 0.70 0.70 0.70 7.04 9.20 0.75 0.84 0.72 0.72 0.81 0.05 CMCC-ESM2 11.61 15.44 0.73 0.85 0.73 0.73 0.82 6.81 8.75 0.71 0.86 0.74 0.74 0.90 0.01 CNRM-CM6-1 19.55 24.60 1.17 0.59 0.31 0.31 0.03 7.54 9.74 0.80 0.82 0.68 0.68 0.65 0.72 CNRM-CM6-1-HR 16.59 21.42 1.02 0.69 0.48 0.48 0.27 8.18 10.58 0.86 0.79 0.62 0.62 0.28 0.79 CNRM-ESM2-1 19.20 24.61 1.17 0.59 0.31 0.31 0.01 7.79 10.08 0.82 0.81 0.66 0.66 0.48 0.55 EC-Earth3 17.33 22.42 1.07 0.67 0.43 0.43 0.13 8.24 10.95 0.89 0.77 0.60 0.60 0.14 0.56 EC-Earth3-Veg 16.35 21.19 1.01 0.71 0.49 0.49 0.30 8.14 10.75 0.88 0.78 0.61 0.61 0.20 0.66 FGOALS-g3 10.87 14.68 0.70 0.87 0.75 0.75 0.88 7.58 9.87 0.81 0.81 0.67 0.67 0.56 0.56 GFDL-CM4 13.07 16.93 0.80 0.82 0.67 0.67 0.61 7.97 10.06 0.82 0.81 0.66 0.66 0.47 0.82 GFDL-ESM4 14.66 18.67 0.89 0.78 0.60 0.60 0.47 8.12 10.62 0.87 0.79 0.62 0.62 0.25 0.67 GISS-E2-1-G 16.27 20.37 0.97 0.74 0.53 0.53 0.33 8.07 10.34 0.84 0.80 0.64 0.64 0.40 0.72 INM-CM4-8 11.70 15.83 0.75 0.84 0.72 0.72 0.79 6.81 9.26 0.76 0.84 0.71 0.71 0.73 0.39 INM-CM5-0 11.10 14.55 0.69 0.87 0.76 0.76 0.92 7.21 8.95 0.73 0.85 0.73 0.73 0.84 0.87 IPSL-CM6A-LR 15.70 19.91 0.95 0.74 0.55 0.55 0.37 7.26 9.54 0.78 0.83 0.69 0.69 0.69 0.52 MIROC6 17.26 22.07 1.05 0.68 0.45 0.45 0.23 8.89 11.41 0.93 0.75 0.56 0.56 0.03 0.40 MPI-ESM1-2-HR 13.49 16.98 0.81 0.82 0.67 0.67 0.58 6.88 8.93 0.73 0.85 0.73 0.73 0.87 0.11 MPI-ESM1-2-LR 14.93 19.05 0.91 0.77 0.59 0.59 0.42 6.98 9.00 0.74 0.85 0.73 0.73 0.84 0.37 MRI-ESM2-0 17.28 22.34 1.06 0.68 0.43 0.43 0.18 9.20 12.38 1.01 0.70 0.48 0.48 0.00 0.11 NorESM2-LM 12.61 16.55 0.79 0.83 0.69 0.69 0.66 7.60 9.84 0.80 0.81 0.67 0.67 0.60 0.69 NorESM2-MM 13.46 17.72 0.84 0.80 0.64 0.64 0.54 8.43 10.72 0.88 0.78 0.61 0.61 0.20 0.87 TaiESM1 11.88 15.84 0.75 0.84 0.71 0.71 0.75 8.07 10.26 0.84 0.80 0.65 0.65 0.41 0.34 UKESM1-0-LL 17.27 22.38 1.06 0.68 0.44 0.44 0.18 8.07 10.34 0.84 0.80 0.64 0.64 0.38 0.29 BCC-CSM2-MR, NorESM2-MM, INM-CM5-0, and GFDL-CM4 performed best among GCMs in simulating historical surface windspeed as compared with monthly ERA5 data over RVLB. According to Akinsanola et al (2021), INM-CM5-0 showed predominantly zero bias in capturing the wind speed and direction as compared to ERA5 in West Africa. Akinsanola et al (2021) suggested that the MME of CMIP6 accurately captured the near-surface wind characteristics throughout the majority of West Africa. Consequently, the MMME of top performing CMIP6 is considered in the following sections. 3.3 Annual cycle of climate variables 3.3.1 Precipitation cycle The average annual cycle of bias corrected historical and future monthly climate variables of observed and MMME over the RVLB are shown in Fig. 2. The bias corrected model datasets captured prominent features of the annual monthly and seasonal precipitation patterns in RVLB associated with the oscillation of the Inter-Tropical Convergence Zone (ITCZ) as described by Diro et al ( 2008 ). Both historical and future precipitation followed bimodal characteristics of precipitation in RVLB. Considering the seasonality of historical, the precipitation peaks in May and August over RVLB (Fig. 2). The MMME captured the seasonal patterns of precipitation in Ethiopia including February to May (FMAM), June to September (JJAS) and September to November (SON) in RVLB (Fig. 2a). The projected precipitation also followed the seasonal variability of the historical observed and simulated precipitation with bi-modal precipitation characteristics separated by minimum precipitation in June (Fig. 2a). However, it is projected in decline of precipitation from March to June, and an increment in JJAS and SON. This may lead to the shift in the water availability for cropping season in FMAM (‘Belg’). The major rainy season, or summer season, occurs in JJAS, contributes 50–80% of annual precipitation, and is the major source of water used in rainfed agriculture and reservoirs, and its shift adds complication to the existing water challenges such as drought and flooding (Korecha and Barnston 2007 ). Additionally, FMAM and SON precipitation is strongly more important for agricultural and socioeconomic activities in Southern Ethiopia (Diro et al 2008 ; Viste et al 2013 ). Overall, the patterns of seasonal precipitation are well-represented, and the simulations realistically reproduced biannual precipitation regimes. Ayugi et al ( 2021a ) also reported better reproducibility of annual rainfall over East Africa by CMIP6 models. Insert Fig. 2 3.3.2 Annual cycle of Temperatures The bias-corrected results for Tmax and Tmin for the historical period are close to the observed in the RVLB. The monthly variability of bias corrected MMME Tmax and Tmin are consistent with the observed (Fig. 2b, 2c). Projected Tmax and Tmin under all SSPs are higher indicating expected increase of air temperature in RVLB in all months (Fig. 2b, 2c; Table S1 ). The increase is naturally larger for stronger forcing scenarios (SSP5-8.5) in 2080s. The increasing signals for Tmax with SSP5-8.5 reflects the greenhouse gas emission levels considered in developing the SSPs (Song et al 2023 ) 3.3.3 Annual cycle of relative humidity, solar radiation and windspeed The observed and simulated solar radiation exhibited seasonal changes (Fig. 2d, Table S1 ) with the strong value occurring in FMAM (a maximum in March) and the weak value occurring in JJAS (a minimum in June and July). The observed daily mean annual cycle of relative humidity shows an increment from February to May, fairly constant from June to October and declining from October to December (Fig. 2e). The historical MMME of CMIP6 accurately represented the seasonal fluctuations of the relative humidity. Wind speed from mean ERA5 and MMME shows a decline from Feb/March to May and July to September, whereas it exhibited an increment from May to July and September to Feb/March. The MMME of CMIP6 captured the mean monthly, seasonal and annual ERA5 (Fig. 2f, Table S1 ). Consistent results were reported for windspeed over China from CMIP6 MME (Wu et al 2020 ; Zha et al 2023 ). 3.5 Projected changes in mean precipitation One of the most significant aspects of how climate change affects sectors with low adaptation capacity and vulnerability is considered to be changes in precipitation which are essential for agriculture, hydrologic modeling, and the climate change impact assessment in RVLB. Projections are made against the baseline period of 1985–2014 for the relative changes of precipitation in future periods based on the MMME of GFDL-CM4, GFDL-ESM4, BCC-CSM2-MR and MPI-ESM1-2-HR. The spatio-temporal distribution of mean values and relative change in annual precipitation are presented in Fig. 3. According to grid point based analysis of the MMME, precipitation is projected to change from 0.6–7.7 (1.5–10.9) % in 2035–2065 (2064–2094) as a percentage of the 1985–2014, respectively under SSP2-4.5 (Fig. 3i, 3j). The areal mean precipitation for RVLB shows an increasing precipitation annually and seasonally (Table S1 ). The projected changes in area-averaged annual precipitation over time in the RVLB is projected to change by − 2.7–7.2 (1.5–15.1) % in 2035–2065 (2064–2094), respectively under SSP3-7.0 (Fig. 3k, 3l). Furthermore, the precipitation under SSP5-8.5 has the largest upward increment with the annual precipitation variation range of − 0.2–7.3 (6.1–20.9) % in 2035–2065 (2065–2094), respectively (Fig. 3m, 3n). Precipitation averaged over the RVLB is projected to increase most under SSP5-8.5, followed by SSP3-7.0, and the least under SSP2-4.5 (Table S1 ). The findings of this study are in agreement with Almazroui et al ( 2020 ), who examined an increasing trend of total precipitation under SSP5-8.5 over North East Africa (NEAF). The results show that large parts of the RVLB will receive an increased precipitation. These findings complement previous analyses by Gebresellase et al ( 2022 ), which projected an increase in bias corrected monthly precipitation by 1.45% −5.51% (2.57%−9.78%) in the Awash basin under SSP5-8.5 in mid (end) of century, respectively. The increase in precipitation could boost effective precipitation totals to augment the high amount of surface and ground water availability to expand agricultural production and other water demanding sectors (Conway and Schipper 2011 ; Tesfaye et al 2019 ) and may reduce frequently occurring drought (WMO 2023 ; Gashaw et al 2024 ). Perhaps, it could result in an excess of soil moisture and potentially cause flooding, waterlogging, and the silting of water infrastructures, soil erosion and nutrient leaching (Tegegn et al 2021; Meresa et al 2022 ). The results indicate the need for implementation of efficient water management strategies in order to lessen the negative effects of climate change on the RVLB's water resources and agricultural productivity. Insert Fig. 3 3.6 Projected changes in mean Temperature The absolute changes of projected mean Tmax using MMME of bias-corrected top performing GCMs (BCC-CSM2-MR, NorESM2-MM, INM-CM5-0 and FGOALS-g3) was examined for future scenario against the historical period as shown in Fig. 4. The predicted mean Tmax is expected to increase over RVLB with 0.77 − 1.02 (1.08 − 1.5) ℃ under SSP2-4.5, 0.93 − 1.27(1.48 − 2.13) ℃ under SSP3-7.0, and 1.03 − 1.02(1.79 − 2.61) ℃ under SSP5-8.5 for 2035–2064 (2065–2094), respectively (Fig. 4). The change in projected Tmax will show higher values over the Northern part of the RVLB under all SSPs as compared with reference period (Fig. 4i-4n). According to observed data (Fig. 4a), the southern part was warmer than the northern part of the RVLB, but CMIP6 GCMs anticipated that the northern part may exhibit a stronger warming. However, the lowland areas with higher Tmax in reference period will continue to receive high Tmax as compared to highlands (Fig. 4a-4h, Table S1 ). According to Alaminie et al ( 2021 ), the mean Tmax for the near (long)-term from MRI-ESM2-0 are expected to increase under the four SSPs in Upper Blue Nile Basin. Additionally, Gashaw et al ( 2024 ) noted an increase in Tmax in Bale eco-region. Rettie et al ( 2023 ) also reported MPI-ESM1-2-HR and CMCC-CM2-SR5 project a mean change of annual Tmax between 0.6 to 1.4°C under SSP3-7.0 in 2050s in Ethiopia. The result strongly suggests that rising temperatures will result in increased evapotranspiration, which might lead to greater moisture loss from soil and plants that results in intensification of water scarcity to negatively impact crop yields and pasture conditions (EPCC 2015; Conway and Schipper 2011 ). The increasing warm temperature negatively affects food systems by shortening growing seasons and increasing water insecurity (Trisos et al 2022 ). Insert Fig. 3 The averaged 30-year change in Tmin from ensemble of BCC-CSM2-MR, CNRM-CM6-1, FGOALS-g3, and EC-Earth3-Veg (MMME) is projected to increase by 1.44 − 1.96 (1.90 − 2.64) ℃ under SSP2-4.5, 1.9 − 2.29 (2.31 − 3.76) ℃ under SSP3-7.0, 1.81 − 2.64 (2.65 − 4.06) ℃ under SSP5-8.5 over 2050s and 2080s (Figs. 5i-5n). The result indicates an increasing warming with higher magnitudes under SSP3-7.0 and SSP5-8.5 in the 2080s over the RVLB. Relatively lower change of Tmin of 0.6 to 2.0°C under SSP3-7.0 in 2050s in Ethiopia (Rettie et al 2023 ). The finding of Gashaw et al ( 2024 ) also revealed an increase in Tmin from CMIP6. The current result is in agreement with Almazroui et al ( 2020 ), who highlighted that the warming trend in mean temperature from CMIP6-MME for the near (long)-term period under SSPs in the region of North East Africa. The increasing temperature will lead to an increasing heat stress to livestock and crops, evapotranspiration demand, which can cause reduction of agricultural productivity and health risks in the RVLB. Insert Fig. 5 3.7 Projected relative humidity, solar radiation and windspeed Averaged over the RVLB, according to the selected ensembles of CMCC-ESM2, INM-CM5-0, MPI-ESM1-2-HR and INM-CM4-8 (MMME), absolute change in mean relative humidity increases by 1.18–2.13 (1.64–2.69) % for SSP2-4.5, 1.35–2.28(2.62–3.2) % for SSP3-7.0, and 2.31–3.22% (4.27–6.04%) for SSP5-8.5 under 2035–2064 (2065–2094), respectively. The increment in relative humidity might be from global warming as warmer temperature increase the water vapor according to Clausius-Clapeyron equation that states an increase in temperature of 1°C can result in a 7% increase in moisture content (Machard et al 2024 ). Insert Fig. 6 The spatial distribution of mean and absolute changes of rsds from MMME of top GCMs (INM-CM5-0, FGOALS-g3, BCC-CSM2-MR and CMCC-ESM2) is illustrated on Fig. 6. Relative to the historical period, rsds is projected to decrease over the future periods across the entire RVLB (Fig. 6). The rsds will decrease by 3.1–4.96 (3.17–6.28) W/m 2 in 2050s (2080s) under SSP2-4.5, respectively. The mean rsds projection showed a decreasing absolute change by 4.4–6.9(6.0-11.3) W/m 2 under SSP5-8.5 in 2035–2064 (2065–2094), respectively. The decline in rsds will be stronger under SSP3-7.0 where it is projected to decrease between 4.9–21.8 (6.2–24.4) W/m 2 in 2050s (2080s), respectively over RVLB (Fig. 7g, 7h). Such a reduction in future rsds was also reported by other studies (Song et al 2023 ; Machard et al 2024 ). The decrease in solar radiation may be attributed to higher reduction of solar irradiance from increasing aerosol concentrations and occasionally from increasing cloudiness and precipitation events (Machard et al 2024 ). In addition, SSP3-7.0 represents pertinent questions about the sensitivity of regional climate to land use and aerosols (O′Neill et al 2016). Insert Fig. 7 Figure 8 displays the future spatiotemporal characteristics of the mean wind speed and its absolute change over RVLB. Results show that MMME of CMIP6 well captured the spatial distributions of the annual ERA5 windspeed (Fig. 8a, 8b). Similar results were reported by Wu et al ( 2020 ). The results also demonstrate minimal change in wind projections in the future. Compared to reference period, mean windspeed is projected to change by − 0.01–0.05(–0.07 − 0.01) m/s under SSP2-4.5 in 2035–2064 and 2065–2094, respectively. Under SSP2-4.5, the spatial patterns of surface wind speed changes in the two periods are almost similar (Fig. 8i and 8j), and characterized by an increase over northern part, and a decrease over southern RVLB. Likewise, 0.01–0.09 m/s under SSP3-7.0 in both periods, and − 0.03–0.14(–0.37 − 0.11) m/s under SSP5-8.5, the decreased mean surface wind speed is found over southern part in both periods (Fig. 8m, 8n). Windspeed is expected to vary over RVLB, with North part experiencing increases, and decrease over southern part under SSP5-8.5 in both periods, and under SSP3-7.0 in 2080s. These projected changes in windspeed could affect patterns of precipitation and temperature that influences agricultural practices and water resource management. Insert Fig. 8 Conclusion This study examined the performance of the 28 CMIP6-GCMs in simulating the historical key climate variables for the reference period 1985–2014, and projecting the future mean climate in two future periods (2035–2064 and 2065–2094) under SSP2-4.5, SSP3-7.0 and SSP5-8.5 experiments over RVLB. The raw GCMs exhibited a significant bias by underestimating or overestimating the reference data. MBCn bias correction was employed to adjust the systematic modeling bias. Comprehensive rating metric (CRM) for bias corrected GCMs based on ME, MAE, PBIAS, RMSE, RSR, d, r, and KGE was used to evaluate the performance of GCMs. Pertaining to CRM, bias corrected GFDL-CM4, BCC-CSM2-MR, GFDL-ESM4 and MPI-ESM1-2-HR outperformed in capturing the monthly precipitation in RVLB. BCC-CSM2-MR, NorESM2-MM, INM-CM5-0, and FGOALS-g3 exhibited superior performance for Tmax. FGOALS-g3, BCC-CSM2-MR, EC-Earth3-Veg and CNRM-CM6-1 revealed better agreement with monthly observed Tmin. CMCC-ESM2, MPI-ESM2-HR, INM-CM5-0 and MPI-ESM2-LR provided the more accurate representation of relative humidity (hurs) over RVLB. Similarly, INM-CM5-0, FGOALS-g3, BCC-CSM2-MR, and CMCC-ESM2 demonstrated stronger performance for surface downwelling solar radiation (rsds). BCC-CSM2-MR, NorESM2-MM, INM-CM5-0, and GFDL-CM4 performed best among GCMs in simulating surface windspeed (sfcWind). The above performance results reveal that the set of top performing GCMs vary in capturing the six climate variables. Thus, the MMME of the above four GCMs of each climate variable is used for climate analysis and projection to minimize uncertainties arising from individual GCM. According to the MMME, precipitation is projected to change from 0.6–7.7 (1.5–10.9) % under SSP2-4.5, − 2.7–7.2 (1.5–15.1) % from SSP3-7.0, and − 0.2–7.3 (6.1–20.9) % under SSP5-8.5 in 2035–2065 (2065–2094), respectively. The predicted mean Tmax is expected to increase over RVLB with 0.77 − 1.02 (1.08 − 1.5) ℃ under SSP2-4.5, 0.93 − 1.27 (1.48 − 2.13) ℃ under SSP3-7.0, and 1.03 − 1.02 (1.79 − 2.61) ℃ under SSP5-8.5 for 2035–2064 (2065–2094), respectively. Tmin is projected to increase by 1.44 − 1.96 (1.90 − 2.64) ℃ under SSP2-4.5, 1.9–2.29 (2.31 − 3.76) ℃ under SSP3-7.0, 1.81 − 2.64 (2.65 − 4.06) ℃ under SSP5-8.5 over 2050s and 2080s. mean relative humidity increases by 1.18–2.13% (1.64–2.69) for SSP2-4.5, 1.35–2.28(2.62–3.2) % for SSP3-7.0, and 2.31–3.22 (4.27–6.04) % for SSP5-8.5 under 2035–2064 (2065–2094), respectively. The rsds will decrease by 3.1–4.96 (3.17–6.28) W/m 2 under SSP2-4.5, 4.9–21.8 (6.2–24.4) W/m 2 under SSP3-7.0, and 4.4–6.9(6.0-11.3) W/m 2 under SSP5-8.5 in 2035–2064 (2065–2094), respectively over RVLB. Mean windspeed is projected to change by − 0.01–0.05(–0.07 − 0.01) m/s under SSP2-4.5, 0.01–0.09 under SSP3-7.0, and − 0.028-0.4(–0.37 − 0.11) under SSP5-8.5 in 2035–2064 (2065–2094), respectively. In conclusion, all the future climate variables show an increment in all SSPs in both periods except rsds in all SSPs and both periods, and windspeed under SSP5-8.5. The projected precipitation shows substantial increment that benefit water and agricultural sectors in the RVLB. More intense and frequent heavy precipitation with short-duration is expected with increasing global warming, that may lead to flash floods. Predictably, a consistently increasing temperatures that will lead to an increased vulnerability for water insecurity, heatwaves and recurrent drought conditions which can cause adverse impact for water demanding sectors. Therefore, this study provides considerately essential information for planning and implementation of effective climate change adaptation and mitigation strategies to cope with the climate hazards. Further investigation on the impact of the changes from six climate variables on the water resources and water security is essential to fully understand and react accordingly. Declarations Acknowledgments The Ethiopian National Meteorology Institute (NMI) is gratefully acknowledged for the provision of the observed precipitation and temperature data. We are thankful ECWMF for ERA5 data sets (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form), and ISIMIP for the EWEMBI data (https://data.isimip.org/10.5880/pik.2019.004) We acknowledge the World Climate Research Program’s (WCRP) Working Group on Coupled Modelling, which is responsible for CMIP6, and we thank the climate modeling groups (GCMs listed in Table 1) for producing and making available their model outputs. The first author is indebtedly grateful to Addis Ababa University and Dilla university for financial support. CRedit Taxonomy- Authors contributions The authors agreed to the manuscript’s submission for publication and each contributed as follows: Yonas Ademe Woldemariam contributed to conceptualization, data curation, formal analysis, methodology, writing the original draft, and writing review and editing. Tekalegn Ayele Woldesenbet contributed to conceptualization, data curation, methodology, supervision, validation, visualization, and writing review and editing. Tenna Alamirew contributed to conceptualization, data curation, methodology, supervision, validation, and writing review and editing. Availability of data The datasets analyzed during the current study are from the National meteorological institute NMI ( http-//www.ethiomet.gov.et/) via an official support letter for observed data. The CMIP6 data sets used in this study are openly available in a WRCP (https://aims2.llnl.gov/search). Funding statement This work is financially supported for the first author from Addis Ababa University and Dilla University. Conflict of interest disclosure We (Yonas Ademe, Tekalegn Ayele Woldesnbet, and Tena Alamirew), hereby declare that there is no financial and non-financial conflict of interest among us and partners on the submitted manuscript. Ethics approval statement Ethics approval was not required to this article as no animal data was used during the current study. Patient consent statement Consent of participate was not required to this article as human data was not used during the study Permission to reproduce material from other sources Permission to reproduce material from other sources was not required for this study. Clinical trial registration Clinical trial registration was not required to this article since no clinical trial data in any form was used during the current study. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5449000","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":387211331,"identity":"6646b8c0-f029-48a1-8ebf-bd37d35a69be","order_by":0,"name":"Yonas Ademe Woldemariam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYPACZhA+ACQkZEjRwpYA0sJDihYeAxCLsBaD470HP/6osZbTbe/5/OpGjQUPA/vhoxvwajlzLlma51i6sdmZs9usc44BHcaTlnYDr5YbOQbSDGyHE7fdyN1mnMMG1CLBY0ZIi/HPH/8O12+7/+aZcc4/4rSYSfC2HU4wu8HD/Di3jQgtkmfOmFnz9qUbbjuTZsac2yfBw0bIL3zHe4xv/vhmLW92/PDjzznf6uT42Q8fw6tF4QCCzSYBJvEpBwH5BgSb+QMh1aNgFIyCUTAyAQDA0Uptf06xggAAAABJRU5ErkJggg==","orcid":"","institution":"Ethiopian Institute of Water Resources, Addis Ababa University","correspondingAuthor":true,"prefix":"","firstName":"Yonas","middleName":"Ademe","lastName":"Woldemariam","suffix":""},{"id":387211333,"identity":"bcb6a04a-d72b-4756-b083-6f7dca81eb60","order_by":1,"name":"Tekalegn Ayele Woldesenbet","email":"","orcid":"","institution":"Ethiopian Institute of Water Resources, Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Tekalegn","middleName":"Ayele","lastName":"Woldesenbet","suffix":""},{"id":387211335,"identity":"9645a8fd-238e-4830-bfb5-7f073d9d3892","order_by":2,"name":"Tena Alamirew","email":"","orcid":"","institution":"Ethiopian Institute of Water Resources, Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Tena","middleName":"","lastName":"Alamirew","suffix":""}],"badges":[],"createdAt":"2024-11-13 18:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5449000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5449000/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-025-05356-8","type":"published","date":"2025-01-17T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71220500,"identity":"c110aa3c-ad8e-4139-bc77-6b2793b7d0f9","added_by":"auto","created_at":"2024-12-12 09:10:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9040782,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of study area with grid points for climate variables, and elevation from DEM as a background. The elevation map is based on the DEM data of 30 m resolution obtained from the Version 3 of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (\u003ca href=\"https://terra.nasa.gov/about/terra-instruments/aster\" target=\"_blank\"\u003eASTER\u003c/a\u003e) Global Digital Elevation Model (\u003ca href=\"https://doi.org/10.5067/ASTER/ASTGTM.003\" target=\"_blank\"\u003eGDEM\u003c/a\u003e (\u003ca href=\"https://www.earthdata.nasa.gov/news/new-aster-gdem/\"\u003ehttps://www.earthdata.nasa.gov/news/new-aster-gdem/\u003c/a\u003e )\u003c/p\u003e","description":"","filename":"Fig1StudyarealocationRVLB.png","url":"https://assets-eu.researchsquare.com/files/rs-5449000/v1/3db6d05c4c83cf48c22d6e14.png"},{"id":71218819,"identity":"e9ccccd7-1efb-482c-b1b8-d139f4f43440","added_by":"auto","created_at":"2024-12-12 09:02:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":424811,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean annual monthly a) precipitation (mm), b) Tmax (℃), c) Tmin (℃), d) relative humidity (%), e) solar radiation (W/m\u003csup\u003e2\u003c/sup\u003e), and f) windspeed (m/s) of observed and historical (1985-2014), future scenario under SSP2-4.5, SSP3-7.0 and SSP5-8.5 in 2035-2064 and 2065-2094, respectively over RVLB.\u003c/p\u003e","description":"","filename":"Fig2Monthlyannualcycleofclimatevariables.png","url":"https://assets-eu.researchsquare.com/files/rs-5449000/v1/05057d3bc8fbe3855eb91d98.png"},{"id":71218827,"identity":"086bfebf-c7f7-4582-aa22-4824ef55e228","added_by":"auto","created_at":"2024-12-12 09:02:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10819028,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean annual precipitation (pr, mm) of observed, historical and SSPs (a-h) and relative change (%) of mean annual precipitation (i-n) over RVLB\u003c/p\u003e","description":"","filename":"Fig3MeanannualandRelativechangeofprecipitation.png","url":"https://assets-eu.researchsquare.com/files/rs-5449000/v1/e4ed4a06b750f1173923ef65.png"},{"id":71218821,"identity":"40154c2f-8900-4991-9ea1-d6fec49bc702","added_by":"auto","created_at":"2024-12-12 09:02:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8251851,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean annual daily Tmax (℃) of observed, historical, SSPs (a-h) and absolute change (℃) of mean annual daily Tmax (i-n) over RVLB\u003c/p\u003e","description":"","filename":"Fig4MeanandabsolutechangeinTmax.png","url":"https://assets-eu.researchsquare.com/files/rs-5449000/v1/5eccadcc3e5720a1c7fdbfe2.png"},{"id":71218826,"identity":"0e81d099-e7fd-4492-9ecd-8d0dc9288d2a","added_by":"auto","created_at":"2024-12-12 09:02:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":8065265,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean annual daily Tmin (℃) of observed, historical, SSPs (a-h) and absolute change (℃) of mean annual daily Tmax (i-n) over RVLB\u003c/p\u003e","description":"","filename":"Fig5MeanandabsolutechangeinTmin.png","url":"https://assets-eu.researchsquare.com/files/rs-5449000/v1/9ef772d2eb1c02f49af0be4e.png"},{"id":71220525,"identity":"86cd356a-912a-416f-8f03-b83e2ee5924d","added_by":"auto","created_at":"2024-12-12 09:10:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":9497250,"visible":true,"origin":"","legend":"\u003cp\u003ethe mean annual daily relative humidity (%) of observed, historical, SSPs (a-h) and absolute change (%) of mean annual daily hurs (i-n) over RVLB\u003c/p\u003e","description":"","filename":"Fig6Meanandabsolutechangeinsolarradiation.png","url":"https://assets-eu.researchsquare.com/files/rs-5449000/v1/ddf7a5c96a046c8c80266ee6.png"},{"id":71220526,"identity":"bfb64eb7-04e5-49f8-b70b-f00c075aeccf","added_by":"auto","created_at":"2024-12-12 09:10:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":9349977,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean annual daily solar radiation (Wm\u003csup\u003e-2\u003c/sup\u003e) of observed, historical, SSPs (a-h) and absolute change (Wm\u003csup\u003e-2\u003c/sup\u003e) of mean annual daily solar radiation (i-n) over RVLB\u003c/p\u003e","description":"","filename":"Fig7Meanandabsolutechangeinrelativehumidity.png","url":"https://assets-eu.researchsquare.com/files/rs-5449000/v1/be07edf141428e7ba31dae22.png"},{"id":71218822,"identity":"5597e100-07d7-4e62-b4b1-71a46612b7c3","added_by":"auto","created_at":"2024-12-12 09:02:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9865716,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean annual daily windspeed (m/s) of observed, historical, SSPs (m/s) and absolute change (m/s) of mean annual daily windspeed (i-n) over RVLB\u003c/p\u003e","description":"","filename":"Fig8MeanandabsolutechangeinWindspeed.png","url":"https://assets-eu.researchsquare.com/files/rs-5449000/v1/7d4b369b09cdb9721f15cf0a.png"},{"id":74284617,"identity":"00b269a0-86d9-422c-b31f-b745f2a6b0ed","added_by":"auto","created_at":"2025-01-20 16:09:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":59697225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5449000/v1/072bd5f6-c802-4549-a28a-f3b628122261.pdf"},{"id":71218824,"identity":"90a7fa86-521b-478a-87f8-8b2af70f9e5d","added_by":"auto","created_at":"2024-12-12 09:02:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2959867,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5449000/v1/00dd583c78ab5e552ed92ec9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation and Projection of CMIP6 Simulations of Climate Variables in the Rift Valley Lakes Basin, Ethiopia","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe anthropogenic climate change is already affecting the human and natural systems across the globe through its contributions to the observed changes in severity and frequency of weather and climate extremes (Eyring et al \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; IPCC \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Climate change has been a serious challenge for vulnerable regions due to their high exposure and low adaptive capacity such as in Africa (Niang et al \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Trisos et al \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). East Africa is one of the hotspots of high human vulnerability to climatic hazards, such as drought and flooding, among the regions projected to be susceptible to climate change (Trisos et al \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; WMO \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, Ethiopia is an extreme example of being impacted by climate change due to its dependence on rainfed agriculture (Conway and Scipper 2011; WB 2021), and worst droughts in 40 years (WMO \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Predictably, climate change will amplify existing stress on water availability, and exacerbate the vulnerability of agricultural systems and land degradation (Niang et al \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Taye et al \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Trisos et al \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; IPCC \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGeneral circulation models (GCMs) have been commonly used for investigating the response of the climate system, and have been applied for an understanding of past, present, and future climate variability and change (Stouffer et al \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Eyring et al \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Coupled Model Intercomparison Project (CMIP) has provided remarkable contributions for simulation and projection of climate change via spatiotemporal variability of climate variables (Eyring et al \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The CMIP of phase sixth (CMIP6) has been designed to produce multi-model datasets to advance knowledge of climate variability and climate change (Eyring et al \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). CMIP6 models have been developed based on CMIP5 models to address major scientific gaps identified (Stouffer et al \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and to provide a set of state-of-the-art GCM simulations to support the IPCC Sixth Assessment Report (Eyring et al \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; IPCC 2021). Moreover, it is reported that the climate models participating in CMIP6 have shown improvements in their models\u0026rsquo; ability to simulate past and present climate in terms of physical parameterizations, model performance, higher horizontal resolution, reproducing climate, and internal variability compared to CMIP5 predecessors (Eyring et al \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the evaluation of CMIP6 GCMs in different regions indicated region specific performance in simulating climate variables.\u003c/p\u003e \u003cp\u003eCMIP6 GCMs have been used to investigate the mean temperature and precipitation (e.g., Almazroui et al \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Alaminie et al \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gebresellase et al \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rettie et al \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gashaw et al \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), surface temperature (Fan et al \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Alaminie et al \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ayugi et al \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), wind speed (Akinsanola et al 2021; Zha et al \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), relative humidity and solar radiation (Li et al \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Song et al \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and estimation of evapotranspiration (Song et al \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, the GCMs from CMIP6 have been applied to assess climate change and variability in different parts of Ethiopia. For instance, Alaminie et al (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified BCC-CSM2-MR and MRI-ESM2-0 as best performing for mean precipitation and maximum temperature from 21 CMIP6 GCMs, respectively and they projected slightly increasing precipitation and warming trend over the Blue Nile basin under four SSPs. Another study in Awash basin by Gebresellase et al (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also selected five CMIP6-GCMs as best performers for the mean climate, and they found an increasing future temperature in all parts of the Awash basin. The studies showed that the CMIP6 GCMs could accurately simulate precipitation and temperatures even though there is considerable variation among regions, seasons, and climate variables. Although robust improvements are reported by researchers, the performance of GCMs shows inconsistency, bias, and discrepancies in simulating observed climate variables from region to region.\u003c/p\u003e \u003cp\u003eAs CMIP6 provides valuable insights into the potential impacts of climate change, it is essential to understand the performance of CMIP6-GCMs, and how well these are used for future projections will be critical to sustainable natural resources management, and climate change adaptation planning and future projection over RVLB. In addition, the above-mentioned studies focused on precipitation and temperature without considering the under-studied relative humidity, solar radiation and windspeed which are large source of uncertainty in global land surface modeling (Li et al \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and significant role in hydrologic cycle, agricultural production, renewable energy and other applications (Wu et al \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Akinsanola et al 2021; Dieng et al \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zha et al \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGCM outputs exhibit substantial systematic modeling errors with differences between simulated and observed climate statistics (Chen et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Maraun \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cannon \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To overcome the problems of biases in GCMs to variety of contexts of applications, bias-correction is necessary to act as an interface between simulations from climate models and impact modeling (Chen et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In hydro-climatic impact studies, numerous approaches of bias correction techniques have been employed (e.g., Gudmundsson et al \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Teutschbein and Seibert \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chen et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cannon 2015, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, quantile mapping (QM) outperforms other bias correction methods that only adjust the variance or mean due to its ability preserve the statistical properties of observations at all quantiles and representing the entire marginal distribution of observed variables (Chen et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gudmundsson et al \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). QM has been extensively used to adjust the biases in GCMs (Ayugi et al \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Dieng et al \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Meresa et al \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rettie et al \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gashaw et al \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs in many parts of Ethiopia, the economy of the inhabitants of the RVLB is heavily dependent on natural resources, and on rain-fed agriculture which is significantly affected by the adverse impacts of climate change (Conway and Schipper \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wagesho et al \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ayalew et al \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is particularly vulnerable to the effects of a warming and drying climate, which could adversely impact crop yields and pastures. The basin is a densely populated area and prone to high climate variability, frequent drought and flash flood events (Viste et al \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tesfamariam et al \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For sustainable adaptation and mitigation planning, CMIP6 could provide valuable insights into the potential impacts of climate change in RVLB.\u003c/p\u003e \u003cp\u003eIn such a vulnerable basin to climate variability and change, studies on the multi-model CMIP6-GCM evaluations and projections are scarce, making it difficult for policymakers and end users to get up-to-date information. In addition, this is the first initial study using CMIP6 to simulate and project the climate variables except studies in Ethiopia as whole by Birhanu et al (2023) on model evaluation, Rettie et al (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) on evaluation and projection of precipitation and temperature, and bias uncorrected GCMs in Abaya-Chamo subbasin (Ersado and Awoke \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This implies the ability of GCMs to reproduce the historical and future climate of RVLB has not been widely studied. Therefore, it is crucial to evaluate the performance of the GCMs to confirm the extent to which they can reproduce the observed climate from multi-GCM ensembles. Hence, this study provides essential information for effective climate change adaptation and mitigation strategies to cope up from devastating impacts of climate related hazards through sustainable natural resources management. The objective of this study is to evaluate the performance of GCMs and project changes in six climate variables (precipitation, Tmax, Tmin, relative humidity, solar radiation and windspeed) using CMIP6 in RVLB for the reference period (1985\u0026ndash;2014), and the two future scenarios of 2050s (2035\u0026ndash;2064) and 2080s (2065\u0026ndash;2094) under SSP2-4.5, SSP3-7.0 and SSP5-8.5.\u003c/p\u003e \u003cp\u003eThis article is organized as follows. Section 2 explains the study area, models, data and methods used in this study. Section 3 presents evaluation results and projection of CMIP6 models with respect to climate variables in RVLB. A conclusion is provided in Section 4.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Description of the study area\u003c/h2\u003e\n \u003cp\u003eThe Rift Valley Lakes Basin (RVLB) is located in the southern part of the Main Ethiopian Rift with an area of about 53,000 km\u0026sup2;. Its elevation ranges from 450m over the southern pastoral area of the rift valley to 4,190 m in the northeast highland (Fig. 1). The basin provides fresh water supply and diverse ecosystems benefits for more than 15 million people who mostly depend on subsistence agriculture. The basin is densely populated with more than 200 people per square kilometer and prone to high climate variability, frequent drought and flood events (Viste et al \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; EPCC 2015; Tesfamariam et al \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Intensive subsistence small holder rainfed crop production (annual and perennial) and livestock rearing are the decisive backbones of the livelihood. Among the food and cash crops, coffee and \u003cem\u003eenset\u003c/em\u003e have economically significant in most parts of the RVLB (Wagesho et al \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tesfamariam et al \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Crops like also cereals including maize teff, wheat barley, vegetables, and fruits are commonly grown (Ayalew et al \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe basin is characterized by a bi-modal precipitation pattern during FMAM (February to May, short rainy season, also locally known as \u0026lsquo;Belg\u0026rsquo;) and JJAS (June to September, also locally known as \u0026lsquo;Kiremt\u0026rsquo;) in highland. The southern part receives its major precipitation during FMAM and SON (September, October and November). The RVLB receives mean annual precipitation of 849 mm with 1021.2 (\u0026plusmn;\u0026thinsp;94.8) mm, 841.2 (\u0026plusmn;\u0026thinsp;85.6) mm, and 670 (\u0026plusmn;\u0026thinsp;86.9) mm over highlands, midlands and lowlands, respectively for period 1985\u0026ndash;2014. The mean annual Tmax in the RVLB ranged from 32.99\u0026deg;C in the lowland to 19.4\u0026deg;C in the highlands. The mean Tmin ranged from 17.47\u0026deg;C in the lowland to 7.58\u0026deg;C in the highlands.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Fig. 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Climate Data\u003c/h2\u003e\n \u003cp\u003eThe performance of CMIP6 models strongly depends on the availability and reliability of the reference data sets (Li et al \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The daily gridded precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) product of Ethiopia known as Enhancing NAtional ClimaTe Services (ENACTS) used as daily observed for bias correction and evaluation of the skill of GCMs were obtained from the Ethiopian National Meteorological Institute (NMI) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ethiomet.gov.et/\u003c/span\u003e\u003c/span\u003e). These data have grid of 0.0375\u0026deg; x 0.0375\u0026deg;. Since actual ground-based wind speed dataset over RVLB are sparse and not publicly available, daily near surface wind speed data (sfcWind) was obtained from the European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis version 5 (ERA5) atmospheric reanalysis product (Hersbach et al \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, Near Surface Relative Humidity (hurs), and Surface Downwelling Shortwave Radiation (rsds) data were obtained from EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI) dataset product for bias-correction (Lange \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eDaily precipitation, Tmax (tasmax), Tmin (tasmin), hurs, rsds and sfcWind data outputs from 28 CMIP6 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) models were downloaded from ESGF Metagrid (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aims2.llnl.gov/search/\u003c/span\u003e\u003c/span\u003e) of the World Climate Research Program (WCRP). The models including the climate variables under both historical CMIP simulations and a corresponding future ScenariMIP (Eyring et al \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; O\u0026rsquo;Neil et al 2016) with daily frequency was considered. For the purpose of comparison, GCMs and observed data were re-gridded to a common grid of 0.25\u0026deg; \u0026times; 0.25\u0026deg; horizontal resolution by using the popular bilinear interpolation method (Amazroui et al 2020; Li et al \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rettie et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, to perform a consistent analysis of the historical and the future scenarios, first member realization outputs (r1i1p1f1/f2) and the Tier-1 (highest priority) scenarios shared socio-economic pathways (SSPs) including SSP2-4.5, SSP3-7.0 and SSP5-8.5 were selected for evaluation and projection (O\u0026rsquo;Neill et al \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Meinshausen et al \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The future climate was projected for 2035\u0026ndash;2064 (2050s) and 2065\u0026ndash;2094 (2080s) in comparison to the baseline period (1985\u0026ndash;2014).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eList of the GCMs from the CMIP6 used in this study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInstitution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eResolution (km)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCESS-CM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSIRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCESS-ESM1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSIRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCC-CSM2-MR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeijing Climate Center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCESM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNCAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCMCC-ESM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCMCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanESM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CM6‐1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CERFACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CM6‐HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CERFACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-ESM2‐1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CERFACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC-Earth3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC-Earth Consortium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC-Earth3-Veg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC-Earth Consortium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGOALS-g3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChinese Academy of Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFDL-CM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFDL-ESM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGISS-E2-1-G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNASA- GISS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINM-CM4-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRussia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINM-CM5-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRussia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstitut Pierre-Simon Laplace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIROC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMIROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax Planck Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax Planck Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRI-ESM2-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeteorological Research Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNESM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNUIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorESM2-LM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorwegian Climate Centre (NCC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorESM2-MM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaiESM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAS-RCEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUKESM1-0-LL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMet Office Hadley Centre (MOHC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert\u003c/strong\u003e Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Model bias correction\u003c/h2\u003e\n \u003cp\u003eBias correction is an essential post-processing procedure that helps to minimize the inherent systematic error in GCMs, and improve the quality of the simulated outputs. The quantile mapping (QM) bias correction technique was applied to adjust the distribution of daily raw output of climate variables with the distribution of daily observed climate variables at each grids using quantile mapping packages on R statistical software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e\n \u003cp\u003eMultivariate bias correction that matches the multivariate distribution using quintile delta mapping (QDM) and the N-dimensional probability density function transform (N-pdft) (MBCn) was used to adjust the bias of precipitation, Tmax, Tmin, hurs, rsds and sfcWind (Cannon et al \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e, 2018, 2023). MBCn transforms all statistical characteristics of the observed distribution to GCM outputs. In addition, QDM has a widely desired feature for studies examining the impacts of climate change through preserving the trends of projections in all quantiles and transforming all features of the observed data distribution to the equivalent distribution from GCM (Maraun \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cannon \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dieng et al \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Marchard et al 2024).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Model performance metrics\u003c/h2\u003e\n \u003cp\u003eThe historical experiments of 28 GCMs were compared with the observed/reanalysis historical climatic variables throughout 1985\u0026ndash;2014 to select the best performing models for each variable using statistical metrics. The Mean Error (ME), Mean Absolute Error (MAE), the Root Mean Squared Error (RMSE), Ratio of the RMSE to the standard deviation of the observations (RSR), percent bias (PBIAS), index of agreement (d), Pearson correlation coefficient (r), and Kling-Gupta Efficiency (KGE) metrics were computed to evaluate the GCMs performance in order to select skillful models in simulating the near-surface climatic variables. The metrics measure the average error magnitude between the simulated and the observed monthly timeseries. The smaller the ME, MAE, PBIAS, RSR, and RMSE the better the performance of the models. Whereas the higher the d, r and KGE close to an optimal value of 1, the models are better performing. These metrics were used to evaluate the performance of CMIP6-GCMs (e.g., Alaminie et al \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ayugi et al \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Berhanu et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rettie et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Simulated monthly timeseries were evaluated and compared against the observed counterparts using ME, MAE, RMSE, RSR, PBIAS, d, r and KGE. These metrics were estimated using hydroGOF r package (Mauricio \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eTo understand and quantify the relative performance of each GCMs, the overall ranking of the GCMs simulation performance was conducted by combining the above metrics obtained with a comprehensive rating metrics (CRM) (Birhanu et al 2023; Gashaw et al \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), which is defined as:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:\\text{C}\\text{R}\\text{M}=1-\\frac{1}{\\text{n}\\text{m}}\\sum\\:_{\\text{i}=1}^{\\text{n}}{\\text{r}\\text{a}\\text{n}\\text{k}}_{\\text{i}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere\u003c/p\u003e\n \u003cp\u003em is the number of models, and n is the number of metrics. The rank varies from 1 for the best-performing model to 28 for the worst model for each metrics. Therefore, the closer CRM is to 1, the better the model performs. Based on the CRM values, ensemble of multi-model mean ensemble (MMME)of top four best performing bias corrected GCMs were used for projection.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Projected change analysis\u003c/h2\u003e\n \u003cp\u003eBased on historical and future climate datasets from CMIP6-GCMs, projected changes in climate variables under the Tier-1 Scenario Model Intercomparison Project (ScenarioMIP) were investigated as described in detail in (e.g., O\u0026rsquo;Neill et al \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Meinshausen et al (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). These scenarios include SSP2-4.5, SSP3-7.0 and SSP5-8.5. A grid point based analysis of the projected changes in climate variables were expressed by the relative percentage changes and absolute change, between future scenario (2035\u0026ndash;2064 and 2065\u0026ndash;2094) to the historical baseline of 1985\u0026ndash;2014 (Taye et al \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). To efficiently minimize the significant uncertainty from individual GCMs, the MMME of the most performing GCMs for each variable was used to project the future climate (Flato et al \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gashaw et al \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Ordinary kriging on ARCGIS 10.8 was used for interpolating the grid point based PBIAS, mean and change values to illustrate the spatial distribution and variation over RVLB.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Bias correction of the GCMs\u003c/h2\u003e\n \u003cp\u003eTo provide an adequate support for informed decision on climate change adaptation, rigorous local scale climate change projections at high spatial and temporal resolutions are required. Fig.\u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e shows the spatial patterns of the bias between the simulated mean annual precipitation and observations for the period 1985–2014. The result shows a considerable bias and inconsistency of raw GCMs in simulating precipitation over the RVLB. Majority of the raw GCMs showed overestimation of precipitation over North of RVLB (for example, MIROC6 by PBIAS = 278%)), and across Southern by INM-CM4-8 (PBIAS = 278%), whereas underestimation (for instance, CNRM-CM6-1-HR (PBIAS= -80%), CNRM-ESM2-1 (PBIAS= -77%), and CNRM-CM6-1 (PBIAS= -76%) around southern and southwest part of RVLB (Fig.\u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Higher biases were also reported by Rettie et al (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) from MIROC6 model for a considerable part of the Ethiopia. The larger bias from the models requests the need for bias correction (Cannon \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Song et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Machard et al \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e)). In this regard, the observed data appears to have been more accurately represented by the bias corrected GCMs (Fig.\u0026nbsp;2a, 3a, 3b).\u003c/p\u003e\n \u003cp\u003eMBCn has improved the performance and confirmed from simulated precipitation of MMME. The spatial distribution of bias adjusted historical precipitation (Fig. 3a, 3b) closely match the observed by reducing the systematic model biases (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). As depicted on Fig. 3a, 3b and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, it is evident that the bias corrected GCMs are closer to the observed than the uncorrected (raw) GCMs, although not being exactly the same as the observed for all GCMs (e.g., CMCC-ESM2, PBIAS = 17.8%). Therefore, it is crucial to evaluate the performance of bias corrected GCMs in simulating the precipitation in order to select subset of best performing GCMs to estimate the multi-model mean ensemble (MMME).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComprehensive rating metrics (CRM) of bias corrected GCMs outputs rank based on ME, MAE (mm), RMSE (mm), PBIAS (%), RSR, d, r and KGE of historical monthly precipitation of CMIP6 simulations as compared to the observed precipitation for period 1985–2014 over RVLB.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eGCMs\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eME\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMAE\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ePBIAS\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eRSR\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eKGE\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eCRM\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMMME\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−1.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMME27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−4.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−5.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGFDL-CM4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−1.7\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e44.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−2.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBCC-CSM2-MR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−2.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e43.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−3.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMPI-ESM-2-HR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−3.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e43.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−4.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGFDL-ESM4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e46.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMIROC6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−1.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e46.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−2.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCMCC-ESM2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−13.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e34.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e45.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−17.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCanESM5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−4.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e42.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−6.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFGOALS-g3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−1.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e50.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−1.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCMCC-CM2-SR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−4.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e45.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−6.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNorESM2-MM\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e37.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e50.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMPI-ESM-2-LR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−0.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e52.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNESM3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−4.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−5.7\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTaiESM1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−11.7\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e49.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−15.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eINM-CM4-8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−4.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e37.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e50.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−6.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEC-Earth3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−6.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e50.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−8.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CM6-HR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−8.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e36.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e49.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−10.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eINM-CM5-0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−7.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−9.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eACCESS-CM2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−8.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e53.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−10.7\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CM6-1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−0.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEC-Earth3-Veg\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−3.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e53.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMRI-ESM2-0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−4.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e39.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−5.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCESM2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−4.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e40.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e51.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−5.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNorESM2-LM\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e61.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-ESM2-1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−0.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e44.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e62.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−0.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−6.7\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−8.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eUKESM1-0-LL\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−4.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e45.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−6.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eACCESS-ESM1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e−6.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e65.0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e−8.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe differences in the Tmax and Tmin between the raw GCMs outputs and observed shows considerable bias ranging from − 32.2% for (e.g., from NESM3 and IPSL-CM6A-LR) to 44% (e.g., from CanESM5 and MIROC6) for Tmax (Fig.S2), and − 7.6% from CNRM-CM6-1 to 131% from NESM3 and NorESM2-LR for Tmin (Fig S3). The result shows the GCMs significantly overestimated Tmin particularly across the northern RVLB with large variability among GCMs and spatial pattern. However, after bias correction using MBCn, all GCMs show a very similar simulation of the observed Tmax and Tmin in terms of desirable PBIAS (~ 0) and ME (~ 0) values (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComprehensive rating metrics (CRM) of bias corrected GCMs outputs rank based on ME, MAE, RMSE, PBIAS (%), RSR, d, r and KGE of historical monthly Tmax, and minimum temperature (Tmin) CMIP6 simulations for period (1985–2014) over RVLB. ME and PBIAS are excluded from this table due desirable values (~ 0)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eCMIP6 GCMs\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eTmax\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eTmin\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGCMs\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMAE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eRSR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKGE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCRM\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMAE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eRSR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKGE\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCRM\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMMME\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMME27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBCC-CSM2-MR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFGOALS-g3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNorESM2-MM\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CM6-1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTaiESM1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CM6-1-HR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-ESM2-1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGISS-E2-1-G\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n 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\u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNESM3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMIROC6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eUKESM1-0-LL\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe simulations of relative humidity vary by PBIAS=–27.4% from UKESM1-0-LL to PBIAS = 61.1% from CNRM-ESM2-1 (Fig S4) from the monthly EWEMBI data. Furthermore, relatively the raw GCMs simulated the solar radiation with narrow range of PBIAS of − 18.3% from CESM2 to 20.1% from MIROC6 as compared to EWEMBI data (Fig S5). It is consistent with Li et al (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) who reported that CMIP6 exhibited the lowest uncertainties and the best performance by simulating net radiation.\u003c/p\u003e\n \u003cp\u003eThe underestimation of windspeed ranges from − 73% predominantly from MPI-ESM1-2-LR and FGOALS-g3 to an overestimation of ~ 179% from CESM2, CanESM5, MIROC6 and IPSL-CM6-LR as compared to ERA5 data over RVLB. Majority of the raw GCMs overestimated sfcWind around the Northern RVLB and underestimated over southwestern part of RVLB (Fig S6). The statistical adequacy of the windspeed simulations is less satisfactory compared to ERA5, particularly concerning metrics for raw GCMs. Large biases in the simulation of historical surface wind speed in the current CMIP6 GCMs were also reported (Wu et al \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) over China.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Climate Models performance in simulating climate variables\u003c/h2\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Precipitation\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents a comprehensive rating metric (CRM) for GCMs based on combination of performance scores (ME, MAE, PBIAS, RMSE, RSR, d, r, and KGE) between the simulated and observed values of monthly precipitation. Among all the GCMs, the bias corrected GFDL-CM4, BCC-CSM2-MR, GFDL-ESM4 and MPI-ESM1-2-HR and their ensemble (MMME) outperformed in capturing the monthly precipitation in RVLB (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Whereas, ACCESS-ESM1-5, UKESM1-0-LL and IPSL-CM6-LR showed poor performance to simulate the precipitation over the RVLB. This indicated that MMME provides robust estimates of the precipitation, and it was considerably better than individual GCMs and ensemble of all GCMs (MME27, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The top GCMs compared well with monthly observed precipitation of RVLB and showed better performance across Ethiopia (Berhanu et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Terefe et al 2023; Rettie et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Alaminie et al \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ersado and Awoke \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), and East Africa (Ayugi et al \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Berhanu et al (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) evaluated that GFDL-CM4 is the best-performing model followed by GFDL-ESM4, NorESM2-MM, and CESM2 in simulating rainfall over Ethiopia. In addition, Terefe et al (2023) reported GFDL-CM4 is the best-performing over Baro basin. Furthermore, MPI-ESM1-2-HR also revealed as the most performing model in simulating JJAS total precipitation over Ethiopia (Rettie et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bias corrected BCC-CSM2-MR also showed better performance for simulating the rainfall climatology of the Bale Eco-Region from the daily to annual temporal scales (Gashaw et al \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, Ersado and Awoke (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) also reported that raw MPI-ESM1-2-HR was among the best performing as compared to CHIRPS data in Abaya-Chamo subbasin. To minimize the uncertainties arising from the weakness of individual models due to systematic errors, there is necessity of using the MMMEs of top performing GCMs in investigating climate simulations and projections (Flato et al \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e, Ayugi et al \u003cspan class=\"CitationRef\"\u003e2021a\u003c/span\u003e, Gashaw et al \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, in this study, the ensemble of the top four GCMs (MMME) was used to the analyze the spatiotemporal changes of precipitation over RVLB.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Temperature\u003c/h2\u003e\n \u003cp\u003eConcerning Tmax under monthly performance evaluation based on CRM, BCC-CSM2-MR, NorESM2-MM, INM-CM5-0, and FGOALS-g3 exhibited superior performance by simulating Tmax (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Whereas, MIROC6, GISS-E2-1-G and MRI-ESM2-0 showed poor performance in simulating observed Tmax over RVLB (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This result is consistent with Feyissa et al (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) for NorESM2-MM over Omo basin, Terefe et al (2023) for INM-CM5-0 over Baro basin, and (Ayugi et al \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e) for FGOALS-g3 over East Africa in reproducing Tmax. In contrast, the performance of MRI-ESM-0 was different from Alaminie et al (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) who reported as best performing for temperature over Blue Nile basin, and Rettie et al (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) showed larger errors from NorESM2-MM in a larger part of Ethiopia for both Tmax and Tmin.\u003c/p\u003e\n \u003cp\u003eThe CMIP6-GCMs were also evaluated in simulating Tmin using CRM. FGOALS-g3, BCC-CSM2-MR, EC-Earth3-Veg and CNRM-CM6-1 exhibited quite consistent agreement with monthly observed Tmin (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). From these GCMs, CNRM-CM6-1 and EC-Earth3-Veg outperformed for simulating Tmin and used for future change analysis in Bale Eco-region (Gashaw et al \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, the ensemble of the four GCMs was used for future projections and change analysis of Tmin in order to minimize the uncertainty arising from the weakness of individual models and to increase confidence in the projection for decision-making.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3 Relative humidity, solar radiation and windspeed\u003c/h2\u003e\n \u003cp\u003eEvaluating GCMs performance based on variety of climate variables can help to find a more reliable set of GCMs for impact modelling (Eyring et al \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among the GCMs, bias corrected CMCC-ESM2, MPI-ESM2-HR, INM-CM5-0 and MPI-ESM2-LR provided the more accurate representation of relative humidity over RVLB (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, INM-CM5-0, FGOALS-g3, BCC-CSM2-MR, and CMCC-ESM2 are found to have stronger performance for surface downwelling solar radiation (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The MMME of relative humidity (Fig. 2e, 6a, 6b), solar radiation (Fig. 2d, 6a, 6b), and windspeed (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig. 2f, 8a, 8b) show overall good agreement in terms of spatial patterns and annual cycles with their respective reference data (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComprehensive rating metrics (CRM) of climate models outputs rank based on mean error (ME), Mean absolute error (MAE), root mean square error (RMSE), ration of RMSE to standard deviation of observed (RSR), degree of agreement (d), Pearson correlation coefficient (r), and Kling-Gupta efficiency (KGE) of historical monthly of historical monthly relative humidity (hurs), near surface dwelling radiation (rsds) and windspeed (sfcWind) CMIP6 simulations for period (1985–2014) over RVLB. ME and PBIAS are excluded from this table due to desirable values (~ 0)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGCMs\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003ersds\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003ehurs\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003esfcWind\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMAE\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eRSR\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eKGE\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eCRM\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMAE\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eRSR\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eKGE\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eCRM\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eCRM\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMMME4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e13.06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e5.98\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMME27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e13.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e6.01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.91\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eACCESS-CM2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e18.32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e23.26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e11.13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e19.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e23.94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e11.09\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBCC-CSM2-MR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e11.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e15.25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCanESM5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e13.87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e18.03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCESM2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e12.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e16.32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e12.47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e16.34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCMCC-ESM2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e11.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e15.44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e6.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CM6-1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e19.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e24.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-CM6-1-HR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e16.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e21.42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCNRM-ESM2-1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e19.20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e24.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEC-Earth3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e17.33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e22.42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEC-Earth3-Veg\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e16.35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e21.19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFGOALS-g3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e14.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.88\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGFDL-CM4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e13.07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e16.93\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.97\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGFDL-ESM4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e14.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e18.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGISS-E2-1-G\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e16.27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e20.37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eINM-CM4-8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e11.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e15.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e6.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eINM-CM5-0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e11.10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e14.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.92\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e15.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e19.91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMIROC6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e17.26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e22.07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e11.41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e13.49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e16.98\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e6.88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.93\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e14.93\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e19.05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e6.98\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMRI-ESM2-0\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e17.28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e22.34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e12.38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNorESM2-LM\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e12.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e16.55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e7.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e9.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNorESM2-MM\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e13.46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e17.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTaiESM1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e11.88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e15.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eUKESM1-0-LL\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e17.27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e22.38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e8.07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e10.34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eBCC-CSM2-MR, NorESM2-MM, INM-CM5-0, and GFDL-CM4 performed best among GCMs in simulating historical surface windspeed as compared with monthly ERA5 data over RVLB. According to Akinsanola et al (2021), INM-CM5-0 showed predominantly zero bias in capturing the wind speed and direction as compared to ERA5 in West Africa. Akinsanola et al (2021) suggested that the MME of CMIP6 accurately captured the near-surface wind characteristics throughout the majority of West Africa. Consequently, the MMME of top performing CMIP6 is considered in the following sections.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Annual cycle of climate variables\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Precipitation cycle\u003c/h2\u003e\n \u003cp\u003eThe average annual cycle of bias corrected historical and future monthly climate variables of observed and MMME over the RVLB are shown in Fig.\u0026nbsp;2. The bias corrected model datasets captured prominent features of the annual monthly and seasonal precipitation patterns in RVLB associated with the oscillation of the Inter-Tropical Convergence Zone (ITCZ) as described by Diro et al (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). Both historical and future precipitation followed bimodal characteristics of precipitation in RVLB. Considering the seasonality of historical, the precipitation peaks in May and August over RVLB (Fig.\u0026nbsp;2). The MMME captured the seasonal patterns of precipitation in Ethiopia including February to May (FMAM), June to September (JJAS) and September to November (SON) in RVLB (Fig.\u0026nbsp;2a).\u003c/p\u003e\n \u003cp\u003eThe projected precipitation also followed the seasonal variability of the historical observed and simulated precipitation with bi-modal precipitation characteristics separated by minimum precipitation in June (Fig. 2a). However, it is projected in decline of precipitation from March to June, and an increment in JJAS and SON. This may lead to the shift in the water availability for cropping season in FMAM (‘Belg’). The major rainy season, or summer season, occurs in JJAS, contributes 50–80% of annual precipitation, and is the major source of water used in rainfed agriculture and reservoirs, and its shift adds complication to the existing water challenges such as drought and flooding (Korecha and Barnston \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Additionally, FMAM and SON precipitation is strongly more important for agricultural and socioeconomic activities in Southern Ethiopia (Diro et al \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Viste et al \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Overall, the patterns of seasonal precipitation are well-represented, and the simulations realistically reproduced biannual precipitation regimes. Ayugi et al (\u003cspan class=\"CitationRef\"\u003e2021a\u003c/span\u003e) also reported better reproducibility of annual rainfall over East Africa by CMIP6 models.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Fig. 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Annual cycle of Temperatures\u003c/h2\u003e\n \u003cp\u003eThe bias-corrected results for Tmax and Tmin for the historical period are close to the observed in the RVLB. The monthly variability of bias corrected MMME Tmax and Tmin are consistent with the observed (Fig. 2b, 2c). Projected Tmax and Tmin under all SSPs are higher indicating expected increase of air temperature in RVLB in all months (Fig. 2b, 2c; Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The increase is naturally larger for stronger forcing scenarios (SSP5-8.5) in 2080s. The increasing signals for Tmax with SSP5-8.5 reflects the greenhouse gas emission levels considered in developing the SSPs (Song et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3 Annual cycle of relative humidity, solar radiation and windspeed\u003c/h2\u003e\n \u003cp\u003eThe observed and simulated solar radiation exhibited seasonal changes (Fig. 2d, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e) with the strong value occurring in FMAM (a maximum in March) and the weak value occurring in JJAS (a minimum in June and July). The observed daily mean annual cycle of relative humidity shows an increment from February to May, fairly constant from June to October and declining from October to December (Fig. 2e). The historical MMME of CMIP6 accurately represented the seasonal fluctuations of the relative humidity. Wind speed from mean ERA5 and MMME shows a decline from Feb/March to May and July to September, whereas it exhibited an increment from May to July and September to Feb/March. The MMME of CMIP6 captured the mean monthly, seasonal and annual ERA5 (Fig. 2f, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Consistent results were reported for windspeed over China from CMIP6 MME (Wu et al \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zha et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Projected changes in mean precipitation\u003c/h2\u003e\n \u003cp\u003eOne of the most significant aspects of how climate change affects sectors with low adaptation capacity and vulnerability is considered to be changes in precipitation which are essential for agriculture, hydrologic modeling, and the climate change impact assessment in RVLB. Projections are made against the baseline period of 1985–2014 for the relative changes of precipitation in future periods based on the MMME of GFDL-CM4, GFDL-ESM4, BCC-CSM2-MR and MPI-ESM1-2-HR. The spatio-temporal distribution of mean values and relative change in annual precipitation are presented in Fig. 3. According to grid point based analysis of the MMME, precipitation is projected to change from 0.6–7.7 (1.5–10.9) % in 2035–2065 (2064–2094) as a percentage of the 1985–2014, respectively under SSP2-4.5 (Fig. 3i, 3j). The areal mean precipitation for RVLB shows an increasing precipitation annually and seasonally (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe projected changes in area-averaged annual precipitation over time in the RVLB is projected to change by − 2.7–7.2 (1.5–15.1) % in 2035–2065 (2064–2094), respectively under SSP3-7.0 (Fig. 3k, 3l). Furthermore, the precipitation under SSP5-8.5 has the largest upward increment with the annual precipitation variation range of − 0.2–7.3 (6.1–20.9) % in 2035–2065 (2065–2094), respectively (Fig. 3m, 3n). Precipitation averaged over the RVLB is projected to increase most under SSP5-8.5, followed by SSP3-7.0, and the least under SSP2-4.5 (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The findings of this study are in agreement with Almazroui et al (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), who examined an increasing trend of total precipitation under SSP5-8.5 over North East Africa (NEAF).\u003c/p\u003e\n \u003cp\u003eThe results show that large parts of the RVLB will receive an increased precipitation. These findings complement previous analyses by Gebresellase et al (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), which projected an increase in bias corrected monthly precipitation by 1.45% −5.51% (2.57%−9.78%) in the Awash basin under SSP5-8.5 in mid (end) of century, respectively. The increase in precipitation could boost effective precipitation totals to augment the high amount of surface and ground water availability to expand agricultural production and other water demanding sectors (Conway and Schipper \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tesfaye et al \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and may reduce frequently occurring drought (WMO \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gashaw et al \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Perhaps, it could result in an excess of soil moisture and potentially cause flooding, waterlogging, and the silting of water infrastructures, soil erosion and nutrient leaching (Tegegn et al 2021; Meresa et al \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The results indicate the need for implementation of efficient water management strategies in order to lessen the negative effects of climate change on the RVLB's water resources and agricultural productivity.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Fig. 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Projected changes in mean Temperature\u003c/h2\u003e\n \u003cp\u003eThe absolute changes of projected mean Tmax using MMME of bias-corrected top performing GCMs (BCC-CSM2-MR, NorESM2-MM, INM-CM5-0 and FGOALS-g3) was examined for future scenario against the historical period as shown in Fig. 4. The predicted mean Tmax is expected to increase over RVLB with 0.77 − 1.02 (1.08 − 1.5) ℃ under SSP2-4.5, 0.93 − 1.27(1.48 − 2.13) ℃ under SSP3-7.0, and 1.03 − 1.02(1.79 − 2.61) ℃ under SSP5-8.5 for 2035–2064 (2065–2094), respectively (Fig. 4). The change in projected Tmax will show higher values over the Northern part of the RVLB under all SSPs as compared with reference period (Fig. 4i-4n). According to observed data (Fig. 4a), the southern part was warmer than the northern part of the RVLB, but CMIP6 GCMs anticipated that the northern part may exhibit a stronger warming. However, the lowland areas with higher Tmax in reference period will continue to receive high Tmax as compared to highlands (Fig. 4a-4h, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). According to Alaminie et al (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), the mean Tmax for the near (long)-term from MRI-ESM2-0 are expected to increase under the four SSPs in Upper Blue Nile Basin. Additionally, Gashaw et al (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) noted an increase in Tmax in Bale eco-region. Rettie et al (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) also reported MPI-ESM1-2-HR and CMCC-CM2-SR5 project a mean change of annual Tmax between 0.6 to 1.4°C under SSP3-7.0 in 2050s in Ethiopia. The result strongly suggests that rising temperatures will result in increased evapotranspiration, which might lead to greater moisture loss from soil and plants that results in intensification of water scarcity to negatively impact crop yields and pasture conditions (EPCC 2015; Conway and Schipper \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). The increasing warm temperature negatively affects food systems by shortening growing seasons and increasing water insecurity (Trisos et al \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Fig. 3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe averaged 30-year change in Tmin from ensemble of BCC-CSM2-MR, CNRM-CM6-1, FGOALS-g3, and EC-Earth3-Veg (MMME) is projected to increase by 1.44 − 1.96 (1.90 − 2.64) ℃ under SSP2-4.5, 1.9 − 2.29 (2.31 − 3.76) ℃ under SSP3-7.0, 1.81 − 2.64 (2.65 − 4.06) ℃ under SSP5-8.5 over 2050s and 2080s (Figs. 5i-5n). The result indicates an increasing warming with higher magnitudes under SSP3-7.0 and SSP5-8.5 in the 2080s over the RVLB. Relatively lower change of Tmin of 0.6 to 2.0°C under SSP3-7.0 in 2050s in Ethiopia (Rettie et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The finding of Gashaw et al (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) also revealed an increase in Tmin from CMIP6. The current result is in agreement with Almazroui et al (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), who highlighted that the warming trend in mean temperature from CMIP6-MME for the near (long)-term period under SSPs in the region of North East Africa. The increasing temperature will lead to an increasing heat stress to livestock and crops, evapotranspiration demand, which can cause reduction of agricultural productivity and health risks in the RVLB.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Fig. 5\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Projected relative humidity, solar radiation and windspeed\u003c/h2\u003e\n \u003cp\u003eAveraged over the RVLB, according to the selected ensembles of CMCC-ESM2, INM-CM5-0, MPI-ESM1-2-HR and INM-CM4-8 (MMME), absolute change in mean relative humidity increases by 1.18–2.13 (1.64–2.69) % for SSP2-4.5, 1.35–2.28(2.62–3.2) % for SSP3-7.0, and 2.31–3.22% (4.27–6.04%) for SSP5-8.5 under 2035–2064 (2065–2094), respectively. The increment in relative humidity might be from global warming as warmer temperature increase the water vapor according to Clausius-Clapeyron equation that states an increase in temperature of 1°C can result in a 7% increase in moisture content (Machard et al \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Fig. 6\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe spatial distribution of mean and absolute changes of rsds from MMME of top GCMs (INM-CM5-0, FGOALS-g3, BCC-CSM2-MR and CMCC-ESM2) is illustrated on Fig.\u0026nbsp;6. Relative to the historical period, rsds is projected to decrease over the future periods across the entire RVLB (Fig.\u0026nbsp;6). The rsds will decrease by 3.1–4.96 (3.17–6.28) W/m\u003csup\u003e2\u003c/sup\u003e in 2050s (2080s) under SSP2-4.5, respectively. The mean rsds projection showed a decreasing absolute change by 4.4–6.9(6.0-11.3) W/m\u003csup\u003e2\u003c/sup\u003e under SSP5-8.5 in 2035–2064 (2065–2094), respectively. The decline in rsds will be stronger under SSP3-7.0 where it is projected to decrease between 4.9–21.8 (6.2–24.4) W/m\u003csup\u003e2\u003c/sup\u003e in 2050s (2080s), respectively over RVLB (Fig. 7g, 7h). Such a reduction in future rsds was also reported by other studies (Song et al \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Machard et al \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The decrease in solar radiation may be attributed to higher reduction of solar irradiance from increasing aerosol concentrations and occasionally from increasing cloudiness and precipitation events (Machard et al \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, SSP3-7.0 represents pertinent questions about the sensitivity of regional climate to land use and aerosols (O′Neill et al 2016).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Fig. 7\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFigure\u0026nbsp;8 displays the future spatiotemporal characteristics of the mean wind speed and its absolute change over RVLB. Results show that MMME of CMIP6 well captured the spatial distributions of the annual ERA5 windspeed (Fig.\u0026nbsp;8a, 8b). Similar results were reported by Wu et al (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The results also demonstrate minimal change in wind projections in the future. Compared to reference period, mean windspeed is projected to change by − 0.01–0.05(–0.07 − 0.01) m/s under SSP2-4.5 in 2035–2064 and 2065–2094, respectively. Under SSP2-4.5, the spatial patterns of surface wind speed changes in the two periods are almost similar (Fig.\u0026nbsp;8i and 8j), and characterized by an increase over northern part, and a decrease over southern RVLB. Likewise, 0.01–0.09 m/s under SSP3-7.0 in both periods, and − 0.03–0.14(–0.37 − 0.11) m/s under SSP5-8.5, the decreased mean surface wind speed is found over southern part in both periods (Fig.\u0026nbsp;8m, 8n). Windspeed is expected to vary over RVLB, with North part experiencing increases, and decrease over southern part under SSP5-8.5 in both periods, and under SSP3-7.0 in 2080s. These projected changes in windspeed could affect patterns of precipitation and temperature that influences agricultural practices and water resource management.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Fig. 8\u003c/strong\u003e\u003c/p\u003e\n \n \n \n \n \n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the performance of the 28 CMIP6-GCMs in simulating the historical key climate variables for the reference period 1985–2014, and projecting the future mean climate in two future periods (2035–2064 and 2065–2094) under SSP2-4.5, SSP3-7.0 and SSP5-8.5 experiments over RVLB. The raw GCMs exhibited a significant bias by underestimating or overestimating the reference data. MBCn bias correction was employed to adjust the systematic modeling bias. Comprehensive rating metric (CRM) for bias corrected GCMs based on ME, MAE, PBIAS, RMSE, RSR, d, r, and KGE was used to evaluate the performance of GCMs.\u003c/p\u003e\u003cp\u003ePertaining to CRM, bias corrected GFDL-CM4, BCC-CSM2-MR, GFDL-ESM4 and MPI-ESM1-2-HR outperformed in capturing the monthly precipitation in RVLB. BCC-CSM2-MR, NorESM2-MM, INM-CM5-0, and FGOALS-g3 exhibited superior performance for Tmax. FGOALS-g3, BCC-CSM2-MR, EC-Earth3-Veg and CNRM-CM6-1 revealed better agreement with monthly observed Tmin. CMCC-ESM2, MPI-ESM2-HR, INM-CM5-0 and MPI-ESM2-LR provided the more accurate representation of relative humidity (hurs) over RVLB. Similarly, INM-CM5-0, FGOALS-g3, BCC-CSM2-MR, and CMCC-ESM2 demonstrated stronger performance for surface downwelling solar radiation (rsds). BCC-CSM2-MR, NorESM2-MM, INM-CM5-0, and GFDL-CM4 performed best among GCMs in simulating surface windspeed (sfcWind). The above performance results reveal that the set of top performing GCMs vary in capturing the six climate variables. Thus, the MMME of the above four GCMs of each climate variable is used for climate analysis and projection to minimize uncertainties arising from individual GCM.\u003c/p\u003e\u003cp\u003eAccording to the MMME, precipitation is projected to change from 0.6–7.7 (1.5–10.9) % under SSP2-4.5, − 2.7–7.2 (1.5–15.1) % from SSP3-7.0, and − 0.2–7.3 (6.1–20.9) % under SSP5-8.5 in 2035–2065 (2065–2094), respectively. The predicted mean Tmax is expected to increase over RVLB with 0.77 − 1.02 (1.08 − 1.5) ℃ under SSP2-4.5, 0.93 − 1.27 (1.48 − 2.13) ℃ under SSP3-7.0, and 1.03 − 1.02 (1.79 − 2.61) ℃ under SSP5-8.5 for 2035–2064 (2065–2094), respectively. Tmin is projected to increase by 1.44 − 1.96 (1.90 − 2.64) ℃ under SSP2-4.5, 1.9–2.29 (2.31 − 3.76) ℃ under SSP3-7.0, 1.81 − 2.64 (2.65 − 4.06) ℃ under SSP5-8.5 over 2050s and 2080s. mean relative humidity increases by 1.18–2.13% (1.64–2.69) for SSP2-4.5, 1.35–2.28(2.62–3.2) % for SSP3-7.0, and 2.31–3.22 (4.27–6.04) % for SSP5-8.5 under 2035–2064 (2065–2094), respectively. The rsds will decrease by 3.1–4.96 (3.17–6.28) W/m\u003csup\u003e2\u003c/sup\u003e under SSP2-4.5, 4.9–21.8 (6.2–24.4) W/m\u003csup\u003e2\u003c/sup\u003e under SSP3-7.0, and 4.4–6.9(6.0-11.3) W/m\u003csup\u003e2\u003c/sup\u003e under SSP5-8.5 in 2035–2064 (2065–2094), respectively over RVLB. Mean windspeed is projected to change by − 0.01–0.05(–0.07 − 0.01) m/s under SSP2-4.5, 0.01–0.09 under SSP3-7.0, and − 0.028-0.4(–0.37 − 0.11) under SSP5-8.5 in 2035–2064 (2065–2094), respectively. In conclusion, all the future climate variables show an increment in all SSPs in both periods except rsds in all SSPs and both periods, and windspeed under SSP5-8.5.\u003c/p\u003e\u003cp\u003eThe projected precipitation shows substantial increment that benefit water and agricultural sectors in the RVLB. More intense and frequent heavy precipitation with short-duration is expected with increasing global warming, that may lead to flash floods. Predictably, a consistently increasing temperatures that will lead to an increased vulnerability for water insecurity, heatwaves and recurrent drought conditions which can cause adverse impact for water demanding sectors. Therefore, this study provides considerately essential information for planning and implementation of effective climate change adaptation and mitigation strategies to cope with the climate hazards. Further investigation on the impact of the changes from six climate variables on the water resources and water security is essential to fully understand and react accordingly.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethiopian National Meteorology Institute (NMI) is gratefully acknowledged for the provision of the observed precipitation and temperature data. We are thankful ECWMF for ERA5 data sets\u003c/p\u003e\n\u003cp\u003e(https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form), and ISIMIP for the EWEMBI data (https://data.isimip.org/10.5880/pik.2019.004)\u003c/p\u003e\n\u003cp\u003eWe acknowledge the World Climate Research Program\u0026rsquo;s (WCRP) Working Group on Coupled Modelling, which is responsible for CMIP6, and we thank the climate modeling groups (GCMs listed in Table 1) for producing and making available their model outputs. The first author is indebtedly grateful to Addis Ababa University and Dilla university for financial support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRedit Taxonomy- Authors contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors agreed to the manuscript\u0026rsquo;s submission for publication and each contributed as follows:\u003c/p\u003e\n\u003cp\u003eYonas Ademe Woldemariam contributed to conceptualization, data curation, formal analysis, methodology, writing the original draft, and writing review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTekalegn Ayele Woldesenbet contributed to conceptualization, data curation, methodology, supervision, validation, visualization, and writing review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTenna Alamirew contributed to conceptualization, data curation, methodology, supervision, validation, and writing review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are from the National meteorological institute NMI ( http-//www.ethiomet.gov.et/) via an official support letter for observed data. The CMIP6 data sets used in this study are openly available in a WRCP (https://aims2.llnl.gov/search).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is financially supported for the first author from Addis Ababa University and Dilla University.\u003cbr\u003e\u0026nbsp;Conflict of interest disclosure\u003c/p\u003e\n\u003cp\u003eWe (Yonas Ademe, Tekalegn Ayele Woldesnbet, and Tena Alamirew), hereby declare that there is no financial and non-financial conflict of interest among us and partners on the submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was not required to this article as no animal data was used during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent of participate was not required to this article as human data was not used during the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to reproduce material from other sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePermission to reproduce material from other sources was not required for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial registration was not required to this article since no clinical trial data in any form was used during the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkinsanola AA, Ogunjobi KO, Abolude AT, Salack S (202) Projected changes in windspeed and wind energy potential over West Africa in CMIP6 models. 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WMO No. 1330, World Meteorological Organization, Geneva, Switzerland.\u003c/li\u003e\n\u003cli\u003eWu J, Shi Y, Xu Y (2020) Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs. JGR: Atmospheres 125(22): e2020JD033611. https://doi.org/10.1029/2020JD033611\u003c/li\u003e\n\u003cli\u003eZha J, Shen C, Wu J, Zhao D, Fan W, Jiang H, Zhao T (2023) Evaluation and Projection of Changes in Daily Maximum Wind Speed over China Based on CMIP6. J clim. 36(5): 1503\u0026ndash;1520. https://doi.org/10.1175/JCLI-D-22-0193.1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bias correction, climate variables, CMIP6, Comprehensive rating metrics, GCM, RVLB","lastPublishedDoi":"10.21203/rs.3.rs-5449000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5449000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe objective of this study is to evaluate the performance of twenty-eight bias-corrected GCMs and project changes in climate variables using CMIP6 from the reference period (1985\u0026ndash;2014), and the two future periods (2035\u0026ndash;2064 and 2065\u0026ndash;2094) under three Shared Socioeconomic Pathways (SSP2-4.5, SSP3-7.0 and SSP5-8.5). Comprehensive rating metric (CRM) based on seven statistical metrics was used to evaluate the performance of GCMs. The multi-model mean ensemble (MMME) of four carefully selected best performing CMIP6-GCMs for each climate variables were used for projection. Considering respective MMMEs, the projected mean precipitation, maximum temperature (Tmax), minimum temperature (Tmin), and relative humidity (hurs), will increase, but solar radiation (rsds) will decline, under all SSPs in both periods as response to global warming. The projected precipitation increase may augment water availability in the Rift valley Lakes Basin (RVLB). However, more intense and frequent heavy precipitation with short-duration may lead to flash floods and landslides to damage crops and infrastructures. In addition, raise on Tmax, Tmin and windspeed may lead to high evapotranspiration demand, recurrent drought, and water insecurity. To properly comprehend and respond appropriately, more research is needed to determine how these changes in climate variables affect sustainable water resources management and water security in RVLB.\u003c/p\u003e","manuscriptTitle":"Evaluation and Projection of CMIP6 Simulations of Climate Variables in the Rift Valley Lakes Basin, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-12 09:02:36","doi":"10.21203/rs.3.rs-5449000/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-06T16:35:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-04T12:51:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238275669714719782184666039550180956184","date":"2024-11-27T17:05:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-26T11:52:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61577087555895773661974122076662778354","date":"2024-11-23T13:20:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271119876625667231050041398782578956775","date":"2024-11-22T13:31:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254305262877810814868157437025368003836","date":"2024-11-21T11:40:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323279244200793092005756899059773977730","date":"2024-11-21T08:42:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338270349479785947308584921357436993886","date":"2024-11-21T01:24:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-20T17:19:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T22:19:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T22:19:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2024-11-13T17:56:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"630bf537-b176-420a-9cfe-0d3fa3b15a60","owner":[],"postedDate":"December 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:01:46+00:00","versionOfRecord":{"articleIdentity":"rs-5449000","link":"https://doi.org/10.1007/s00704-025-05356-8","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2025-01-17 15:57:30","publishedOnDateReadable":"January 17th, 2025"},"versionCreatedAt":"2024-12-12 09:02:36","video":"","vorDoi":"10.1007/s00704-025-05356-8","vorDoiUrl":"https://doi.org/10.1007/s00704-025-05356-8","workflowStages":[]},"version":"v1","identity":"rs-5449000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5449000","identity":"rs-5449000","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}