Simulation of temperature and precipitation under the climate change scenarios: Integration of a GCM and machine learning approaches

This study aims to discuss the potentials of machine learning methods such as artificial neural network (ANN), least squares support vector machine (LSSVM), and relevance vector machine (RVM) in downscaling of simulations of a general circulation model (GCM) for monthly temperature and precipitation of the Demirkopru Dam located in the Aegean region of Turkey. The predictors are obtained from ERAInterim re-analysis data. The best performed downscaling model is integrated into European Centre Hamburg Model (ECHAM5) with A2 future scenario. The results are then discussed to assess the probable climate change effects on temperature and precipitation.