Downscaling diurnal temperature over west and southwest Iran : A comparison of statistical downscaling approaches

This study aimed to forecast temperature variations in the western and southwestern part of Iran using a general circulation model artificial neural networks (ANN). The data included mean diurnal temperatures from synoptic stations, National Centers for Environmental Prediction/ Center Atmospheric Research (NCEP/NCAR) reanalysis data, outputs third-generation global climate model, Hadley Centre Coupled Model version 3 (HadCM3), under A2 B2 scenarios baseline period (1961–1990). first (1961–1975) second 15 years (1976–1990) were used calibration validation, respectively. Both models, however, produced reliable estimates at plain stations with neither outperforming other due their negligible errors. However, network results mountain showed lower error rate than statistical downscaling (SDSM) outputs. All all, we can say that there was larger amount atmospheric models (AGCMs) mountainous regions. According AGCMs, studied on rise. In fact, this increase more noticeable stations. be attributed proximity sea, latitude, intensive industrial activities (especially, extraction petroleum production products) taking place near