Extended abstractIntroduction: Climate change has a significant impact on water resources and the environment, which is reflected in agriculture, society, and economy. The use of General Circulation Models (GCMs) with downscaling Models is a method for assessing Climate change. Considering the placement of South Khorasan Province and the city of Birjand in the arid region of Iran, population growth, industrial and mining development, and the pursuit of sustainable agriculture, it is essential to assess the effects of Climate change on essential meteorological parameters. The objective of this study is to compare the performance of historical Models NCEP and ECMWF in downscaling temperature parameters for the Birjand County and investigate the changes in this parameter until 2030 using the top Model and the SSP245 scenario with the CanESM5 Model.Materials and Methods: In this research, to compare the performance of two GCMs, NCEP and ECMWF, in downscaling temperature parameters, daily temperature data from the Birjand synoptic station for the period from 1990 to 2021 were used as the baseline period. Additionally, to evaluate the performance of these two GCMs, the statistical downscaling Model SDSM was utilized. To assess the performance of these two Models, evaluation criteria such as NS, KGE, RMSE, and BR2 were employed.Results and Discussion: To investigate and compare the performance of two GCMs, NCEP and ECMWF, daily average temperature data from the Birjand synoptic station were used from the January 1990 to the September 2021. The data from 1990 to the January 2008 were considered for calibration, and data from the January 2008 to the September 2015 were used for validation. Both NCEP and ECMWF Models had 26 parameters, and for downscaling, the parameters with the highest correlation with observed temperature were selected among these 26 parameters using the R software and the HydroGof package. Additionally, evaluation criteria such as NS, RMSE, KGE, and BR2 were used to assess the Models' performance in calibration and validation sections. The closeness of variance and mean values of time series generated by the NCEP and ECMWF Models to the variance and mean values of observed time series in the entire simulation period was examined using F and T tests. The results of the calibration section showed that the two Models, NCEP and ECMWF, exhibited similar performance since the values of evaluation criteria NS, RMSE, KGE, and BR2 for the ECMWF Model were calculated as 0.69, 4.86, 0.85, and 0.7, respectively, and for the NCEP Model, they were 0.70, 4.79, 0.85, and 0.7, respectively. Since box plots, mean values, and standard deviations have a high capability in deciding the degree of dispersion and similarity between two time series, box plots, mean values, and standard deviations of the generated time series and observed time series in the calibration and validation periods were used to assess the similarity and closeness of the time series. The results of the evaluation criteria in the validation section showed that the ECMWF Model outperformed the NCEP Model, with values of evaluation criteria NS, RMSE, KGE, and BR2 for the ECMWF Model being calculated as 0.69, 4.9, 0.85, and 0.73, respectively, and for the NCEP Model, 0.67, 5.3, 0.83, and 0.7, respectively. Overall, the results indicated that the ECMWF Model had a better performance and was selected as the superior Model. Therefore, to simulate and predict the average temperature parameter, the parameters mslp, P500, P5-f, P5-u, P850, and P8-u from the ECMWF Model were used. Consequently, it is predicted that the average temperature will increase by approximately 3 degrees Celsius compared to the statistical baseline period in the next 8 years.Conclusion: The results indicate that based on the evaluation criteria, the ECMWF Model performs relatively better in estimating the average temperature of Birjand County compared to the NCEP Model. Moreover, the analysis of box plots, mean values, and standard deviations of the generated time series in the calibration and validation sections showed that both Models produced similar patterns of dispersion, minimum, maximum, and mean values compared to the observed time series. However, the ECMWF Model exhibited relatively better performance in terms of mean and variance values of the generated data on a monthly basis in the calibration and validation periods. As a result, the ECMWF Model was selected as the superior Model for simulating and predicting the average temperature of Birjand County for the years 2022 to 2030 under the SSP245 emission scenario using the CanESM5 Model. The predicted results indicate that the average temperature of Birjand County is expected to increase by approximately 3 degrees Celsius compared to the statistical baseline period.

In recent years, the importance of Climate prediction has increased as a scientific source for understanding Climate change and evaluating its consequences in political and economic decisions. Providing predictions with less uncertainty, especially for precipitation and temperature is of considerable importance for policymakers in time periods from several months to several decades. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-Model investigation into decadal Climate prediction, predictability and variability. The DCPP consists of three components (A, B, and C). Component A comprises of the production and analysis of an extensive archive of retrospective forecasts. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-Model forecasts, and Component C involves the organization and coordination of case studies of particular Climate shifts and variations, both natural and naturally forced (Boer et al. 2016). The aim of this study is to predict precipitation extremes using the decadal Climate Prediction Project contribution to the Coupled Model Intercomparison Project Phase 6 ((CMIP6)) for the period 2021 to 2028 over Iran. For this purpose, two types of data including 77 synoptic stations and three DCPP Models (BCC-CSM2-MR, MPI-ESM1-2-HR, and MRI-ESM2-0) with a horizontal resolution of 100 km were used. The precipitation output of DCPP Models, each with nine variants (27 members) were used for two time periods, including Hindcast (1981-2019) and Forecast (2021-2028). To evaluate DCPP Models, we used the Root Mean squared error (RMSE), the Pearson correlation coefficient (PCC), the Mean Bias Error (MBE), the Percent bias (PBIAS), and the Taylor diagram methods. In addition, Direct Model Output (DMO) was corrected by the Delta Change Factor (DCF) method, and the Independent Weighted Mean (IWM) was used to generate a multi-Model ensemble from 27 members. In this study, the ETCCDI indices including days with Heavy precipitation (R10mm), days with Very heavy precipitation days (R20mm), Simple daily intensity (SDII), The maximum 1-day precipitation amounts (Rx1day), The maximum 3-day precipitation amounts (Rx3day), The maximum 5-day precipitation amounts (Rx5day) were calculated to analyze precipitation extremes for all regions of Iran. Furthermore, the evaluation of the DCPP Models showed that the output of mentioned Models is acceptable for all regions of Iran. Also, the performance of (CMIP6)-DCPP-MME is higher than the individual Models. The result of the prediction of precipitation extremes showed that the six studied extreme precipitation indices will increase for the next decade. The Southwest and Northeast are the two hotspots of positive anomaly. In contrast, the southern coast of the Caspian Sea for the R10mm index will experience a negative anomaly for the next decade. The findings show that the southeastern region of Iran, from the eastern borders to the north of the Strait of Hormuz, will be the main area of negative precipition anomalies in the country in the next decade. So that the indices of days with heavy (R10mm) and very heavy (R20mm) precipition will decrease by 2.7 and 0.3 days, and daily precipition intensity (SDII) will decrease by 2.6 mm/day.

Background and Objective: Considering the not very suitable state of the country's water resources as well as the phenomenon of Climate change and its effects, simulating the state of Climate change in the future and evaluating its effects in order to reduce vulnerability and deal with it can be very important in future decisions. In this regard, in order to reduce the negative effects of Climate change and benefit from its possible positive effects in watersheds, various adaptation strategies are presented. In this research, adaptation to Climate change in the agricultural sector under the (CMIP6)-SSP Climate change scenarios has been investigated and evaluated. Considering the characteristics of the Hashtgerd region and the risks that threaten agriculture in the region, this research tries to have a comprehensive view of this system. For this purpose, adaptation strategies to reduce the negative effects of Climate change in the agricultural sector were evaluated. Material and Methodology: In this research, the SWAT Model was used to simulate and evaluate the adaptive strategy in Hashtgerd region in 2018. To Model Climate change conditions in the region, the NorESM2-MM Climate Model related to the 6th IPCC report and different SSP scenarios (SSP1. 26, SSP2. 54, SSP3. 70, SSP 5. 85) were used and minimum and maximum temperature data and the precipitation were downscaled for the years 2020 to 2049. After calculating the changes in temperature and rainfall compared to the current conditions, the values of these changes were applied to the SWAT Model in order to investigate its effect on the water resources of Hashtgerd region. Finally, the values of water stress and crop performance were estimated under the conditions of Climate change. Findings: The results indicated an average increase in water stress and also a decrease in yield of crops other than corn in all SSP scenarios. After evaluating the effects of Climate change in the region, in order to adapt to these changes in the agricultural sector, two adaptation strategies were used 1) The strategy of changing the cultivation pattern from tomato and alfalfa crops to wheat and barley and 2) Changing the cultivation pattern from tomato and alfalfa to corn. These strategies were evaluated with criteria such as changes in water stress and yield of crops compared to BAU conditions. Discussion and Conclusion: The results showed that changing the cultivation pattern to wheat and barley has reduced the water stress of regional crops. In general, in the strategy of changing the cultivation pattern, the yield of all three crops, wheat, barley and corn, was increased compared to the BAU strategy. In the strategy of changing the cultivation pattern to corn, a significant reduction in water stress was estimated and, accordingly, the yield of this product was acceptable. This increase in performance is due to the reduction of water stress caused by the increase in available water.

This paper investigates future changes in annual mean precipitation and air temperature across the Volga River basin, which serve as significant drivers of Climate-induced changes in the Volga River's discharge, the primary input to the Caspian Sea. The thirteen Global Climate Models (GCMs) outputs under four Shared Socioeconomic Pathways (SSPs) scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) from the Sixth phase of Coupled Model Intercomparison Project ((CMIP6)) are used for this study. In the historical period (1950-2014), using comprehensive rating metrics and Taylor diagram, the GCMs are ranked according to their ability to capture the temporal and spatial variability of precipitation and air temperature. The Multi-Model Ensemble (MME) is generated, and bias-correction techniques are utilized to reduce the uncertainties and correct the biases in (CMIP6) outputs. Bias-correction techniques are assessed in the historical period and the average of proper methods utilized for future Projections (2015-2100). In the 21st century, future Projections show that the Volga River basin could mainly experience a temperature increase of 0.4°C to 7.5°C, alongside a precipitation rise of 0.7% to 37%, depending on the scenarios considered. Comparison of future Projections with an observational dataset from 2015 to 2017 indicates that the SSP2–4.5 is more likely scenario to represent the future Climate of the Volga River basin.

Due to inherent limitations of global Climate Models, their outputs are significantly biased in comparison to observed values which could provide unreliable Climate Projections. This study evaluates the performance of 10 global Climate Models of the Coupled Model Intercomparison Project Phase 6 ((CMIP6)) for simulating precipitation in the Rafsanjan study area over calibration (1986-2005) and validation (2006-2014) period. For correcting simulated precipitation, various quantile mapping-based bias correction methods applied in these two periods. Evaluating the performance of various Climate Models and quantile mapping-based bias correction methods and approaches is carried out through multiple statistical metrics including NSE, PBIAS, MAE, and KGE as well as Taylor's diagram. Finally, simulated precipitation of selected Model extracted for Projection period under SSP1-2.6, SSP2-4.5 and SSP3-7.0 scenarios and corrected by suitable bias correction method. Results showed that the MPI-ESM1-2-LR Model has better performance in simulating precipitation over calibration and validation periods compared to other Climate Models. The results of evaluating the performance of quantile mapping-based bias correction methods in both periods also showed that bernlnorm method performs better than others for the correction of simulated precipitation by Climate Models. In addition, the evaluation results of quantile mapping approaches including NTP, PT, and DDT in these periods demonstrated that NTP and PT have an acceptable performance compared to the DDT approach. Present study can help to improve the credibility of future Climate Projections using (CMIP6) Climate Models.

Heat waves are among the most dangerous weather threats related to global warming and Climate change. Two databases were used to predict the spatial changes in the intensity of heat waves in East Azarbaijan province. The daily data of the maximum temperature in 5 synoptic stations of the province inlcuding Tabriz, Maragheh, Jolfa, Ahar and Mianeh for the time period from 1981 to 2021 AD as the period of historical-base data were used. The output of the selected CanESM Model under the dual economic-social scenario SSP1 is the result of the Coupled Model Intercomparison Project Phase 6 ((CMIP6)) in the future period from 2022 to 2065. The validation of the data of the basic period with the future period was done with standard measures and with the step-by-step regression technique, the intensity of heat waves in the province was explained. The results indicate that the intensity of heat waves will increase until 2065 in all the investigated stations and it will cover a large area of ​​the province. So, in the next half of the century, the intensity of heat waves in Tabriz will be 1.3 °C, in Maragheh will be 1 °C, in Julfa will be 0.7 °C, in Ahar will be 1 °C and in Mianeh it will be 1.4 °C. Moreover, with the warming of the earth's air due to the impact of global Climate changes, smaller heat waves join together and will create more intense, bigger, and lasting heat waves. The results showed that with the decrease in latitude in this province and the proximity to low-lying and low-altitude areas, the frequency and intensity of heat waves will also increase.

Global warming is a gradual increase in the Earth's temperature generally due to the greenhouse effect caused by increased levels of carbon dioxide. Global warming has had significant consequences for human life, significantly affecting agricultural production, ecosystems, and water resources. The Climate system of the Earth responds to a perturbation to the top of the atmosphere radiative balance through a change in temperature. This imbalance constitutes a radiative forcing of the Climate system, and the magnitude of the response is determined by the strength of the forcing and the net radiative feedback. Equilibrium Climate Sensitivity (ECS) is an estimate of the eventual steady-state global warming at double CO2 and Transient Climate Response (TCR) is the mean global warming predicted to occur around the time of doubling CO2 in GCM and ESM runs for which atmospheric CO2 concentration is prescribed to increase at 1% per year.   This study aimed to evaluate the performance of Climate Model Intercomparison Project Phase 6 ((CMIP6)) Models by selecting 30 Models considering TCR and ECS and to investigate temperature spatial distribution and annual trends in Iran. The performance of these Models has been investigated with data from Iran’s fifty-one synoptic stations for the historical period (1980-2014) using the Taylor diagram and box plot. The results showed that most (CMIP6) Models have good performance in simulating spatial temperature patterns. However, in the area-averaged, 73% of the selected Models have estimated the temperature of the country as less than the station data. In general, more than 56% of the Models showed a correlation higher than 0.5 compared to station data in the area-averaged temperature of Iran. Four Models including CanESM5, INM-CM5-0, TaiESM1, and UKESM1-0-LL have shown the highest performance in estimating the temperature in Iran. The area-averaged annual temperature trend, which was examined by the modified Mann-Kendall test, showed that the temperature trend of (CMIP6) Models is increasing along with the observational data for all Models. Most (CMIP6) Models, however, have simulated higher warming rates in the historical period, which differs from station data. These differences cannot be explained by internal Climate variability (ICV), and the equilibrium Climate sensitivity of most (CMIP6) Models has created a greater rate of warming in the Models. For example, Models such as CanESM5 and UKESM1-0-LL, which showed the highest trend, had the highest ECS and TCR among all.

Extended Abstract Background: In recent years, changes in Climate and land use have led to fluctuations in water resources. These changes have affected the river flow, environment, and drinking and agricultural water. Land use change has four important effects on watersheds, namely changes in peak flow characteristics, changes in total runoff volume, changes in water quality, and changes in hydrological balance. To prevent natural disasters, it is important to identify the current conditions and predict the future situation. Overcoming these crises and reducing their adverse effects are only possible in the shadow of management, planning, and relying on practical knowledge. The present study aimed to determine the impact of Climate change and land use on the river flow in the Talar basin between 2020 and 2050. Methods: The effects of future land use, Climate changes, and their combined effect in the Talar basin (Mazandaran province) have not been seriously investigated using the Sixth Climate change report. Therefore, this study analyzed data based on (CMIP6) Climate change scenarios and land use Projections for 2035 and 2050. First, the SWAT Model was used to evaluate the effects of Climate and land use on the river flow in the Talar River basin. After calibration and validation of the Model using the best parameters from 2001 to 2020, (CMIP6) data were downscaled based on six Models and Projected under two scenarios SSP2-4.5 and SSP5-8.5. The scale of atmospheric general circulation Models was reduced using two methods: the delta method and quantile mapping (Qm). These methods were chosen due to the large scale of the Models. In this research, the Markov prediction Model (CA-Markov) was used to simulate and predict land use changes for the years 2035 and 2050. Precipitation and temperature data obtained from Climate change and land use scenarios were entered into the SWAT Model to predict the average monthly flow during the years 2020-2035 and 2020-2050. Results: Calibration and validation at the Kiakola station as the output of the Talar watershed showed that the Nash-Sutcliffe index (NSE) had efficiencies of 0.8 and 0.76, respectively. The best values of the validation indices were obtained by the INM Model. The Delta method for downscaled precipitation data and the Qm method for downscaled minimum and maximum temperatures showed better evaluation values. For example, the presented tables show that the values of RMSE, NRMSE, and MAE for the rainfall of the Kiakola station are 2.185, 0.0402, and 1.716, respectively, using the Delta method. All these values show the good accuracy of these downscaling methods for SWAT Model inputs to predict the streamflow in the Talar River basin. These methods were implemented for all the studied stations, and the downscaled values of the aforementioned parameters were used to predict the streamflow of the Talar River basin at the Kiakola station. Conclusion: The predicted results for 2035 and 2050 show a decrease in the runoff volume, wetlands, and urban land. Therefore, land use activities in the future should be based on appropriate land use development and land use regulation to reduce the long-term adverse effects of land use changes. In the Talar River basin, land use changes are mainly controlled by internal factors, such as agricultural land expansion and urbanization, while Climate change is regarded as an external factor. Both have an important role in changing the hydrological processes of the basin. This study evaluates the combined effects of land use and future Climate changes on the water balance in the Talar River basin. The combination of land use change and Climate change has a more obvious effect on surface runoff. On a monthly scale, runoff from surface runoff decreases significantly across seasons, indicating that more extreme events (i.e., droughts) could potentially occur in the future. With land use changes, these effects can only be reduced by less than 20%. Therefore, more measures (for example, soil conservation) are needed in addition to land use planning to increase infiltration and aquifer nutrition and, subsequently, reduce risks from land use and Climate change impacts. This research presents the effects of changes in land use and Climate on the available water in the Talar River basin in the future. Furthermore, this paper presents a study on the use of the SWAT Model in hydrology to help the scientific field. The findings of this study can also be useful for officials in reducing water stress through proper management of land use in the future. The results indicate that the average monthly streamflow of the Talar River basin has decreased due to land use changes, such as the expansion of urban areas and the reduction of agricultural land. In the future, changes in land use and land cover (LULC) may affect streamflow. The main drivers of LULC changes include agricultural development, deforestation, urban planning, land tenure policy, and organization development.

Climate change directly affects the hydrological components and water resources and plays an important role in exacerbating potential hazards such as drought and flood. Therefore, it is necessary to study the effects of Climate change on hydrological components such as runoff. In this study, the runoff in Tashk-Bakhtegan basin, as one of the most important ecological basins in the country, was investigated in terms of Climate change using the SWAT Model. Simulation was performed for the near future (2021-2050) by applying Climate change conditions in GFDL-ESM2M and IPSL_CMA5_LR Models under RCP2. 6 and RCP8. 5 scenarios and in GFDLESM4 and IPSL_CMA6_LR Models under SSP1-2. 6 and SSP5-8. 5 scenarios. The calibration and validation results of the SWAT Model using R2, NSH and RMSE indices were in the ranges of (0. 70-0. 99), (0. 51-0. 98) and (0. 9-14. 4 m3/s), respectively which indicated the high accuracy of calibration and validation of the Model. Examination of the status of climatic variables of precipitation and minimum and maximum temperature in the conditions of Climate change showed an increase in temperature (1. 51-2. 91 ° C) for all Models and scenarios and a decrease in precipitation (0. 05-11. 15 percent) in most Models and scenarios. Simulation by SWAT hydrological Model in Climate change conditions showed runoff decline in all 4 stations under SSP scenarios and runoff rise in 3 stations under RCP scenarios. Given that the Climate data of SSP scenarios have recently been made available, the results of this study can be useful to extend the research to the effects of these scenarios on important basins of the country and as a result of policy and planning of water resources under influence Climate change.

The aim of the study is to evaluate the changes of mean temperature and precipitation parameters using IPCC Sixth Report Models ((CMIP6)) under SSP scenarios (SSP1-2.6, SSP2-4.5 and SPP5-8.5) during the period of 2022-2100 in Kashan Plain. The mean temperature and precipitation data was obtained from 7 stations (Kashan, Kavir-e-Hosseinabad, Kamu, Ardestan, Alavi, Noushabad and Sensen) in Kashan plain considering the base period of 30 years (1984-2014). Also, 7 Models were selected from the Models of the Sixth report ((CMIP6)). The post-processing of the output of the Models was carried out using the linear ratio method. Nash-Sutcliffe indices (NSE), root mean square error (RMSE) and correlation coefficient (r) were used to determine the accuracy of the Models. The annual trend of changes was investigated using Mann-Kendall test. Finally, the mean of IPSL-CM6A-LR and BCC-CSM2-MR Models outputs was used to simulate mean temperature and precipitation changes in the future period. According to the results, in all of the studied stations, precipitation in the coming period will have a decreasing trend compared to the base period. The mean temperature will also increase in the future period compared to the base period. In Kavir-e-Hossein Abad, Ardestan, Noush Abad and Sen Sen stations, the intensity of temperature increase will be higher than Kashan, Kamu and Alavi. According to the predicted conditions, it is necessary to pay attention to comprehensive policies in the field of adapting to Climate change in Kashan Plain.