ABSTRACT A Bayesian statistical model,developed,to produce,probabilistic projections of regional climate change using observations,and ensembles,of general circulation models,(GCMs) is applied to evaluate the prob- ability distribution of global mean,temperature,change,under different forcing scenarios. The results are compared,to probabilistic projections obtained using optimal fingerprinting techniques that constrain GCM projections by observations. It is found that, due to the different assumptions underlying these statistical approaches, the predicted distributions differ significantly in particular in their uncertainty ranges. Results presented,herein demonstrate,that probabilistic projections of future climate are strongly dependent,on the assumptions,of the underlying,methodologies.