Projections of precipitation and temperature from Global Climate Models (GCMs) are generally the basis for assessment of the impact of climate change on water resources. The reliability of such assessments, however, is questionable, since GCM projections are subject to uncertainties arising from inaccuracies in the models, greenhouse gas emission scenarios, and initial conditions (or ensemble runs) used. The purpose of the present study is to quantify these sources of uncertainties in future precipitation and temperature projections from GCMs. To this end, we propose a method to estimate a measure of the associated uncertainty (or error), the square root of error variance (SREV), that varies with space and time as a function of the GCM being assessed. The method is applied to estimate uncertainty in monthly precipitation and temperature outputs from six GCMs for the period 2001–2099. The results indicate that, for both precipitation and temperature, uncertainty due to model structure is the largest source of uncertainty. Scenario uncertainly increases, especially for temperature, in future due to divergence of the three emission scenarios analyzed. It is also found that ensemble run uncertainty is more important in precipitation simulation than in temperature simulation. Estimation of uncertainty in both space and time sheds lights on the spatial and temporal patterns of uncertainties in GCM outputs. The generality of this error estimation method also allows its use for uncertainty estimation in any other output from GCMs, providing an effective platform for risk-based assessments of any alternate plans or decisions that may be formulated using GCM simulations.