The climate system is very complex and has made the modeling and predicting/projecting face many challenges. Although climate variability may be detected and identified through a time series of observations, it cannot express the interaction of various components of the Earth's climate system. GENERAL CIRCULATION MODELS (GCMs) are essential for simulating the physical processes governing the atmosphere and the interaction of the components involved in the Earth's climate system. Statistical downscaling extracts empirical relationships between small-scale observational variables (often at the station level) and the direct GCM output by applying three approaches: Perfect Prognosis (PP), Model Output Statistics (MOS), and Weather Generators (WGs). Bias correction, widely used in climate change studies, is the MOS statistical downscaling approach. To clarify the role of using the inappropriate method and software in increasing uncertainty, two scaling methods from the model output statistics (MOS) approach are compared to correct the bias of the minimum and maximum temperatures. In this research, the outputs of R and CMhyd software are compared to check the uncertainty caused by using inappropriate software. The output of the EC-Earth3-CC model for two variables of the minimum and maximum temperatures was examined using CMhyd and R software. Examining the results showed that the CMhyd software has a significant error in both extracting the direct model output and the bias correction method. For example, the PBIAS of direct output of maximum temperature in Abadan was 2.10%, while CMhyd software gives 5.10%. The result of this research shows the need to use the correct methods and software for processing the output of GCMs.

Climate is a complex system that is affected by changes in climatic parameters. By predicting and examining the range of changes in meteorological parameters in the future, it is possible to adopt appropriate solutions to reduce the harmful effects of climate change. Using atmospheric GENERAL CIRCULATION MODELS is the most reliable method. In this study, precipitation, maximum and minimum temperatures of five synoptic stations of Birjand, Qaen, Nehbandan, Ferdows and Tabas, for the base period of 1988 to 2005 as well as the outputs of six climate MODELS of CanESM2, GFDL-CM3, CSIRO-MK3, MPI-ESM - LR, MIROC-ESM and GISS-ES-R, were collected under RCP8.5 and RCP4.5 emission scenarios for a 16-year period (2020-2035) and downscaled using the LARS-WG5.0 model. Then, using the RMSE and MAE statistical indices, the quality of the down-scale representation was evaluated. Afterwards, by calculating the climate classification indices of De Martonne and Amberger, the province was classified with the help of GIS software. De Martonne classification indicates that the climate of the province will not change in the near future compared to the base period while based on the classification of Amberger and under all six MODELS and both scenarios, Birjand, Qaen and Ferdows cities are predicted to have temperate climate and Tabas city is expected have a hot and mild desert climate. For Nehbandan city, the GFDL-CM3, CSIRO-MK3 and GISS-ES-R MODELS of the fifth report under the RCP4.5 scenario predicted a moderate climate and the rest of the large-scale MODELS predicted a moderate desert climate.

IntroductionHeat wave is an extremely important event related to temperature and has a great impact on human health. With the current trend of global warming, heat waves are likely to appear in the future with more frequency and intensity in most areas of the world. Iran has always been exposed to heat waves due to its special climatic conditions. Therefore, forecasting the future heat waves of the country in order to plan the environment and deal with possible risks is essential. The main goal of the current research is to simulate and predict Iran's heat waves based on GENERAL CIRCULATION MODELS. MethodologyIn this research, to identify heat waves, the "Heat Wave Magnitude Index Daily" (HWMId) was used based on the maximum daily temperatures of 44 synoptic stations in the country in a 31-year period (1985 to 2015). Also, in order to simulate the daily maximum temperature and predict the future heat waves of the country with this index, the data of the CanESM2 GCM under the RCP4.5 scenario between the years 2015 to 2045 has been downscaled with SDSM. The input data of SDSM includes historical data of CanESM2 GCM (1961 to 2005), NCEP reanalysis data (1961 to 2005) and CanESM2 GCM data (2006 to 2100) under RCP4.5 scenario. Based on the HWMId, a heat wave is a wave greater than or equal to three consecutive days with a maximum temperature above the daily threshold in the reference period. The magnitude of the heat wave in each day is calculated based on the maximum temperature of that day and the 25th and 75th percentiles of the annual maximum temperature time series in the reference period, and the magnitude of each heat wave is the sum of the magnitude values of all days of that wave. The waves were extracted in each season, based on the separate percentile threshold of that season in each station. Annual values of each station were also obtained from seasonal values. Finally, annual and seasonal maps of average "magnitude" and "number" of observed and simulated heat waves of the country with HWMId and their time trend charts were prepared and analyzed. ResultsIn GENERAL, there is a good agreement between observation and simulated maps. The average frequency of predicted annual waves in different regions of the country is between 2 and 12 occurrences, most of which are located on Shiraz, Shahrekord and Omidiyeh stations. The biggest and greatest heat waves in the future are predicted first for summer and then with a significant difference for winter and spring. Different patterns of the spatial distribution of predicted heat waves can be observed in Iran. In spring and summer, as well as on an annual scale, the maximum "magnitude" and "number" of the simulated heat waves are concentrated on the southwest and the western half of Iran and Gorgan station. In autumn, the center of the maximum is located in the inner regions of Iran and it stretches in the form of an oval from the north-west to the south-east of the country. In terms of the relationship with geographical factors, in summer by moving to higher latitudes and in spring by moving to the east of the country, we see a significant decrease in heat waves. The number and magnitude of heat waves in the country will increase in annual and seasonal scales until 2045, and the highest rate of increase is in summer. The most heat waves are predicted for the year 2043 with the number of 10.5 events. DiscussionNormally, it is expected that the intensity and frequency of heat waves will be higher in the southern regions of the country. But according to the output of the model used in this research, different patterns of seasonal and annual distribution of predicted heat waves can be observed in Iran. This is related to the index used to define heat waves in this research, that is, the HWMId index, which has a percentage basis, and for this reason, the distribution patterns of the phenomenon in the country are out of the uniform and expected state; In such a way that even in the cold seasons of the year, heat waves occur in different regions of the country. For example, winter accounts for 23% of the total heat waves and 26% of the predicted severe heat waves. Nevertheless, summer is still the leading number of predicted heat waves in the country with 41% frequency. Despite the relative concept of HWMId in the definition of heat wave, the relationship between the occurrence of heat waves and some important geographical factors in the country is in accordance with the expectation, so that in the summer by moving to higher latitudes and in the spring by moving to the east of the country, we see a significant decrease in heat waves. Most likely, this reflects the role of external factors in the occurrence of heat waves in Iran, especially the greater impact of the Azores high pressure on the south-west and south of the country, which, of course, requires more studies. ConclusionAlthough heat waves occur in most regions of the country, in all maps, the cores of most heat waves can be seen in the center and southwest of the country around the provinces of Fars, Khuzestan and Hormozgan, and in some cases in the west of the country, which become more frequent and intense in summer. It should be acknowledged that the ability of all MODELS, including the model used in this research, to communicate between all the factors and elements influencing Iran's heat waves, both in terms of time and space, is limited. Therefore, in order to achieve better results, it is suggested to test the ability of other MODELS and scenarios in estimating the country's heat waves.

Atmospheric GENERAL CIRCULATION MODELS, due to the large scale of their computing networks, able to predict the parameters of water and weather are not the point scale, So scientists interface tool called the Weather Generator model was developed that can based on the use of cholera numerical model output, the climate change Vaystgah point scale meteorological study and evaluation contract. GCM outputs used in this study, data from the model ECHO-G is under the A1 scenario. and the results on the eight synoptic stations including Khuzestan, Abadan, Ahvaz, Bostan, Dezful, Masjed Soleiman, Omidiyeh, and Ramhormoz was Safi-Abad. The results of comparing the values of the 2005-1986 monitoring compliance Vmdl significant and suggests that the ability of climate MODELS to simulate data. Model output was determined by examining the area of study is very different responses to climate change0 Values and monitor the results of modeling three arguments, minimum temperature and maximum monthly temperature show that the greatest increase in minimum temperature in Abadan station 18.8 ° C, maximum temperature reduction station Hdaksrdr Bostan -1.8o C, Most rainfall Ramhormoz station with 1.03 mm in the period 2010-2029 has been statistically. The results also suggest that the greatest change in Nsrbarsh in Srasrmntqh studies. A total of eight stations studied, with annual rainfall will increase with decreasing temperature.

In recent years, the severe fluctuations in precipitation have affected various parts of the country. On the southern coasts of the Caspian Sea, precipitation as one of the important climatic parameters has undergone changes due to global climate change. In the present study, we tried to evaluate the effect of climate change on rainfall in this region by applying a suitable model. In this study, observational period rainfall (1961-2001) was analyzed. the output of the HadCM3 model was used. At first, seven synoptic stations were selected and their data were analyzed in terms of accuracy, and length of statistical period, and lost data was restored. The AOGCM data were simulated using the SDSM model and the rainfall values were simulated for the observation period. After confirming the matching of the simulated data with observational data, the values of the Future (2039-2011) is estimated. The estimation errors of the SDSM model were calculated monthly by MBE and MAE criteria, and then compared. The output of the SDSM model was used to study the total annual precipitation in days with rainfall of more than 1 mm in the observation period and the upcoming period (2011-2039) by the R-Climdex model and the values of the PRCPTOT index Became zoning in the Future. The results showed that the model error in season with high rainfall is more than seasons with low rainfall. On a monthly scale, the maximum error occurred in the months of September, October, November and December. The maximum error in the fall and the minimum error was calculated in the spring and April and May months. According to the results, the total annual rainfall in the period of 2039-2011 will decrease in Anzali, Babolsar, Gorgan and Noshahr stations and rainfall will increase in stations of Astara, Ramsar and Rasht. Geographical distribution of selected were 5 sites in the Khuzestan, 20 sites in Bushehr, 24 sites in Hormozghan and 12 sites in Sistan and Baluchistan provinces. In total, 9000 sites were selected with a 2 km2 were suitable for large scale microalgae cultivation. The total area of these sites were estimated to be 18000 km2. The highest number of proper sites were found in Hormozghan province and lowest numbers of sites were found in Khuzestan province. The availability of technical service, carbon dioxide point resources from oil and gas units are an advantages for microalgae related activities in the Bushehr and Khuzestan provinces. The higher quality of water in the Sistan and Baluchistan province is an advantages for development of microalgae biomass production in the area.

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.

Climate change impacted of the amount, timing and type of precipitation, also was impacted on water quality, increased droughts, increased demand for water, changes in the management of water resources, as well as sea level rise and its complications. Climate change also had great influence on the temperature changes so that maximum and minimum values and extreme temperature. The aim of the present study was to evaluate the impact of climate change on rainfall, minimum and maximum temperature use with 15 of atmospheric GENERAL CIRCULATION model under two scenarios A1B and B1 in the period 2039-2011. For this purpose, the use of beta statistical distribution of rainfall changes, minimum and maximum temperatures were calculated from 15 GENERAL CIRCULATION model, the probability of 20, 50 and 80 percent, respectively. The results showed that the probability of 20 to 80 percent under both scenarios A1B and B1 minimum and maximum temperatures are rising and the rain is falling. The minimum and maximum temperatures under A1B increased more than B1 scenario and precipitation reduced under B1 more than A1B scenario. The results showed that 19 to 22% decrease in precipitation and minimum temperature of 13 to 20% and a maximum temperature of 2. 4 to 6. 4 percent increase compared to baseline the Tuyserkan catchment.

BACKGROUND AND OBJECTIVES: Jordan faces water resource challenges due to its arid climate and high population density. This study selects GENERAL CIRCULATION MODELS for the Zarqa River Basin to project future temperature variations under the Shared Socioeconomic Pathways 2-4.5 and 5-8.5 scenarios across four periods: 2015-2040, 2041-2060, 2061-2080, and 2081-2100, assessing climate change impacts on water resources.METHODS: The statistical downscaling model was used to project temperature variations for the two Shared Socioeconomic Pathways over the four periods. The model’s predictors were derived from GENERAL CIRCULATION MODELS and reanalysis datasets. Results showed a strong correlation between temperatures in the Zarqa River Basin and the selected GENERAL CIRCULATION MODELS. The model successfully replicated temperature characteristics during both the calibration (1983-2000) and validation (2001-2014) periods. Projections were made for six stations within the Zarqa River Basin.FINDINGS: Among the selected GENERAL CIRCULATION MODELS, United Kingdm Earth System Modelling project and Hadley Centre Global Environmental Model 3—Global Coupled configuration 3.1 predicted the most rapid temperature increases. High-emission scenarios (Shared Socioeconomic Pathway 5-8.5) forecast larger temperature rises than low-emission scenarios (Shared socioeconomic pathway 2-4.5). The northern Zarqa River Basin is projected to warm more than the southern region, with significant increases expected by the 2090s compared to the 2050s. Minimum temperatures are increasing at twice the rate of maximum temperatures. By 2100, maximum temperatures in the Shared Socioeconomic Pathway 2-4.5 scenario are expected to rise by 3.44-4.91 degrees Celsius, while in the Shared Socioeconomic Pathway 5-8.5 scenario, increases will range from 5.5-6.2 degrees Celsius.CONCLUSION: The study successfully developed a Statistical Downscaling Model for future temperature projections in the Zarqa River Basin under the Shared Socioeconomic Pathway scenarios. The results suggest that the Zarqa River Basin will experience a hotter and drier climate, with more significant temperature increases expected by the late twenty-first century. These findings can inform regional hydrological and environmental modeling and help assess ecosystem sustainability.

Soil moisture is an important factor in hydrological processes. In this study, the uncertainty of AOGCM MODELS to estimate soil moisture were investigated by SWAP model for the future period of 2099-2080. The climatology data were produced by ten AOGCM MODELS and two emission scenarios of A2 and B1. Subsequently, the data were downscaled by LARS_WG model and then the resulting data were used in SWAP model. The research results showed that during the post-growth weeks, the INMCM3 and NCARPCM MODELS had the highest and lowest amounts of soil moisture, respectively. The uncertainty of annual soil moisture indicated that the INMCM3 model had the highest uncertainty band for A2 and B1 scenarios, and the GISS-ER and CGCM3T47 MODELS had the lowest uncertainty band for A2 and B1 scenarios, respectively. Also, by comparing the moisture in soil depths of 60 cm and 30 cm, it was determined that the moisture in the depth of 60 cm would be higher compared to the depth of 30 cm.