Increasing in greenhouse gases such as carbon dioxide in the atmosphere could cause global warming and changes in air temperature and temporal and spatial scatter of precipitation (Mc- Cabe and Wolock, 2002; Moula, 2006). Mean air temperature of the earth was increasing since the 1850s by about 0.5 °C (Nicholls et al., 1996). These changes of precipitation and tem- perature can have significant effects on various sections such as agriculture, energy utilization, and human welfare (Watson et al., 1996). Ac- cording to the IPCC climate change scenarios showed that the temperature is often increased and precipitation totals have increased in high northern latitudes and decreased in lower mid- latitude regions (IPCC, 2001). These changes im- pact the growth and development of crops in a number of ways. Temperature is a key element of evaporative and transpirative demand, partic- ularly for tropical regions (Challinor et al., 2007). Water demand in agriculture and urban sectors may change in future due to climate change, so climate prediction in coming years will provide an insight of water requirement in various sec- tors. The large-scale General Circulation Models (GCMs) are appropriate and primary tool for pre- dicting and investigating the changes of climatic parameters. These models are not applicable in the studies of spatial and temporal changes for climatic parameters at regional and local scales because of their coarse resolution. Iran is located in arid and semiarid region of the world. Due to low rainfall and high potential evapotranspiration, Iran has an average annual precipitation of 242 mm, which is less than one third of the world average. This precipitation is under conditions in which 179 mm of rainfall is directly evaporated. In other words, 71% of the precipitation is lost due to evaporation, while annual potential evap- oration of the country is between 1500 and 2000 mm (Shoaei and Heidari, 2006). Precipitation in Iran is occurred mostly from October to March (Nazemosadat et al., 2006). The main goal of this research was to estimate the change of pre- cipitation and temperature for Kashafrood basin under possible future global warming. The future monthly precipitation, maximum and minimum temperatures were estimated from the output of CGCM2 and HAdCM3 for two socio-economic scenarios (A2 and B2) using statistical downs- caling method (ASD model). This procedure was repeated for three future time periods including 2010–2039, 2040–2069, and 2070–2099. The usefulness of downscaling method depends on the ability of capturing the impacts of climate variability. It was found that the ASD model is a useful tool for downscaling large-scale climate model into the regional climatic parameters. The ASD model performed the most similar scenarios that varied with seasons. On the average differ- ence between two scenarios (A2 and B2), was the same, while the differences among four sea- sons were significant. The comparison of down- scaled data set under B2 scenario for minimum and maximum temperatures for basin showed that the downscaling model produced less warm- ing over the nearly 100-year simulation than A2 by using both GCMs models. The downscaled minimum and maximum temperatures from both GCMs models indicated the global warm- ing for all cases was consistent with the recent observation and increased with concentration of greenhouse gases. Through increasing the tem- perature and changing precipitation pattern, and subsequently evapotranspiration, the climate change will influence the agricultural section. The reliability of precipitation at critical phasestion, Iran has an average annual precipitation of 242 mm, which is less than one third of the world average. This precipitation is under conditions in which 179 mm of rainfall is directly evaporated. In other words, 71% of the precipitation is lost due to evaporation, while annual potential evap- oration of the country is between 1500 and 2000 mm (Shoaei and Heidari, 2006). Precipitation in Iran is occurred mostly from October to March (Nazemosadat et al., 2006). The main goal of this research was to estimate the change of pre- cipitation and temperature for Kashafrood basin under possible future global warming. The future monthly precipitation, maximum and minimum temperatures were estimated from the output of CGCM2 and HAdCM3 for two socio-economic scenarios (A2 and B2) using statistical downs- caling method (ASD model). This procedure was repeated for three future time periods including 2010–2039, 2040–2069, and 2070–2099. The usefulness of downscaling method depends on the ability of capturing the impacts of climate variability. It was found that the ASD model is a useful tool for downscaling large-scale climate model into the regional climatic parameters. The ASD model performed the most similar scenarios that varied with seasons. On the average differ- ence between two scenarios (A2 and B2), was the same, while the differences among four sea- sons were significant. The comparison of down- scaled data set under B2 scenario for minimum and maximum temperatures for basin showed that the downscaling model produced less warm- ing over the nearly 100-year simulation than A2 by using both GCMs models. The downscaled minimum and maximum temperatures from both GCMs models indicated the global warm- ing for all cases was consistent with the recent observation and increased with concentration of greenhouse gases. Through increasing the tem- perature and changing precipitation pattern, and subsequently evapotranspiration, the climate change will influence the agricultural section. The reliability of precipitation at critical phasestion, Iran has an average annual precipitation of 242 mm, which is less than one third of the world average. This precipitation is under conditions in which 179 mm of rainfall is directly evaporated. In other words, 71% of the precipitation is lost due to evaporation, while annual potential evap- oration of the country is between 1500 and 2000 mm (Shoaei and Heidari, 2006). Precipitation in Iran is occurred mostly from October to March (Nazemosadat et al., 2006). The main goal of this research was to estimate the change of pre- cipitation and temperature for Kashafrood basin under possible future global warming. The future monthly precipitation, maximum and minimum temperatures were estimated from the output of CGCM2 and HAdCM3 for two socio-economic scenarios (A2 and B2) using statistical downs- caling method (ASD model). This procedure was repeated for three future time periods including 2010–2039, 2040–2069, and 2070–2099. The usefulness of downscaling method depends on the ability of capturing the impacts of climate variability. It was found that the ASD model is a useful tool for downscaling large-scale climate model into the regional climatic parameters. The ASD model performed the most similar scenarios that varied with seasons. On the average differ- ence between two scenarios (A2 and B2), was the same, while the differences among four sea- sons were significant. The comparison of down- scaled data set under B2 scenario for minimum and maximum temperatures for basin showed that the downscaling model produced less warm- ing over the nearly 100-year simulation than A2 by using both GCMs models. The downscaled minimum and maximum temperatures from both GCMs models indicated the global warm- ing for all cases was consistent with the recent observation and increased with concentration of greenhouse gases. Through increasing the tem- perature and changing precipitation pattern, and subsequently evapotranspiration, the climate change will influence the agricultural section. The reliability of precipitation at critical phases-tion, Iran has an average annual precipitation of 242 mm, which is less than one third of the world average. This precipitation is under conditions in which 179 mm of rainfall is directly evaporated. In other words, 71% of the precipitation is lost due to evaporation, while annual potential evap- oration of the country is between 1500 and 2000 mm (Shoaei and Heidari, 2006). Precipitation in Iran is occurred mostly from October to March (Nazemosadat et al., 2006). The main goal of this research was to estimate the change of pre- cipitation and temperature for Kashafrood basin under possible future global warming. The future monthly precipitation, maximum and minimum temperatures were estimated from the output of CGCM2 and HAdCM3 for two socio-economic scenarios (A2 and B2) using statistical downs- caling method (ASD model). This procedure was repeated for three future time periods including 2010–2039, 2040–2069, and 2070–2099. The usefulness of downscaling method depends on the ability of capturing the impacts of climate variability. It was found that the ASD model is a useful tool for downscaling large-scale climate model into the regional climatic parameters. The ASD model performed the most similar scenarios that varied with seasons. On the average differ- ence between two scenarios (A2 and B2), was the same, while the differences among four sea- sons were significant. The comparison of down- scaled data set under B2 scenario for minimum and maximum temperatures for basin showed that the downscaling model produced less warm- ing over the nearly 100-year simulation than A2 by using both GCMs models. The downscaled minimum and maximum temperatures from both GCMs models indicated the global warm- ing for all cases was consistent with the recent observation and increased with concentration of greenhouse gases. Through increasing the tem- perature and changing precipitation pattern, and subsequently evapotranspiration, the climate change will influence the agricultural section. The reliability of precipitation at critical phases of crop development, counts for variation agri- cultural potential. Interannual or interseasonal precipitation was major challenge to rain fed ag- ricultural procedures (Feddema and Freire, 2001; Nicholson, 2001). The ASD model was capable of simulating current climate to study the future climate change due to the atmospheric projec- tions.

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