  Multi-model projections in climate studies are performed to quantify
uncertainty and improve reliability in climate projections. The challenging
issue is that there is no unique way to obtain performance metrics, nor is
there any consensus about which method would be the best method of combining
models. The goal of this study was to investigate whether combining climate
model projections by artificial neural network (ANN) approach could improve
climate projections and therefore reduce the range of uncertainty. The
equally-weighted model averaging (the mean model) and single climate model
projections (the best model) were also considered as references for the ANN
combination approach. Simulations of present-day climate and future projections
from 15 General Circulation Models (GCMs) for temperature and precipitation
were employed. Results indicated that combining GCM projections by the ANN
combination approach significantly improved the simulations of present-day
temperature and precipitation than the best model and the mean model. The
identity of the best model changed between the two variables and among
stations. Therefore, there was not a unique model which could represent the
best model for all variables and/or stations over the study region. The mean
model was also not skillful in giving a reliable projection of historical
climate. Simulation of temperature indicated that the ANN approach had the best
skill at simulating present-day monthly means than other approaches in all
stations. Simulation of present-day precipitation, however, indicated that the
ANN approach was not the best approach in all stations although it performed
better than the mean model. Multi-model projections of future climate
conditions performed by the ANN approach projected an increase in temperature
and reduction in precipitation in all stations and for all scenarios.
