Long Lead Rainfall Prediction Using Statistical Downscaling and Arti cial Neural Network Modeling

Long lead rainfall prediction is important in the management and operation of waterresources and many models have been developed for this purpose. each of the developed models hasits special strengths and weaknesses that must be considered in real time applications. in this paper, eldand general circulation models (gcm) data are used with the statistical downscaling model (sdsm)and the articial neural network (ann) model for long lead rainfall prediction. these models have beenused for the prediction of rainfall for 5 months (from december to april) in a study area in the southeastern part of iran. the sdsm model considers climate change scenarios using the selected climateparameters in rainfall prediction, but the ann models are driven by observed data and do not considerphysical relations between variables. the results show that sdsm outperforms the ann model.