Synoptic approach to forecasting and statistical downscaling of climate parameters (Case study: Golestan Province)

The present study attempts to introduce a method of statistical downscaling with a synoptic view. The precipitation data of Golestan Province has been used for the years 1971 to 2010. Employing multivariable regression, this study models the precipitation gauges in the station scale, by making use of 26 predicting components of model HadCM3, on the basis of two A2 and B2 scenarios. However, the minimum predicting components for precipitation in station scale included 26 components for one grid to 390 atmosphere circulation components for the 15 suggested grids. Nevertheless, results indicate minimum error, related to the precipitation models, based on projecting components of the studies of 15 grids. By applying this selected method, the precipitation gauges for 2020 to 2040 has been simulated. General results of the precipitation changes for the yearly decennial average of Golestan Province indicates additive stream of this component, based on both A2 and B2 scenarios. Yet this yearly decennial addition of precipitation go with seasonal and annual changes, i.e. getting drier in summer as well as its subsequent increase in draught issue on one hand, and increased centralization of precipitations in the winter and lack of its proper distribution during year on the other. As a result, changes in local patterns of precipitations throughout the province is promising for maximum increase of precipitation for the farthest southwest area of Golestan, greatly potential for decreasing precipitation of sub eastern area.