The impacts of climate change and variability have been an important concern for sustainable agriculture in the U.S. and the world in terms of crop profit (food security in many regions) and environmental preservation. However research on the impacts is still inconclusive and understanding the impacts is not sufficient for appropriate decision making on adaptation options. This situation is partially due to ineffective research approaches. For instance, most of the existing approaches deal with the effect of individual Global Climate Models (GCMs) without a degree of probability attached. Although those approaches may be appropriate for sensitivity analyses and help to define a plausible range of outcomes, none is likely to define the range of plausible adaptive capacities that might emerge in response to climate change scenarios due to the difficulty of handling the uncertainty of climate change. The major uncertainties in projected ranges of regional climate arise from three main sources: (1) Emission scenarios, influenced by economic activity, population growth and technology; (2) Global climate sensitivity, measured by the sensitivity of GCMs to greenhouse gas forcing; and (3) the reliability of the outcomes from various GCMs. Moreover Regional variability occurs between models as different regional responses, and within models through chaotic behaviors and modes of climate variability, especially multi-decadal variability. Direct output from an individual GCM is subject to the sensitivity and forcing within the individual GCM scenario, which has no further degree of probability attached. If a single scenario is used to model a particular impact, the results may be fairly precise but are conditional on that single scenario, and are unlikely to be representative of other possible futures. At best, a range of projected climate change bounded by its high and low extremes can be used to produce a range of impacts, results that are often too broad to be of practical use in planning for adaptation. Furthermore, the GCM scenarios do not provide information about changes in interannual variability or intermonthly variability. The quantification of climate change uncertainty including the range of the global warming impacts and local climate variability remains a research challenge. In summary, such a methodology is needed that will project ranges preserving local patterns of change while being scaled for different assumptions of climate sensitivity and greenhouse gas emissions and containing a level of probability. This paper describes a methodology to quantify the uncertainty with climate change and the corresponding vulnerability with agricultural production in a particular region. The application of known probability distributions has been used to solve problems in many fields, such as economics, insurance and gambling, where, by applying historical data, statistical methods are used to forecast the probability of a particular set of outcomes. For climate change, projections of climate are derived from physical models (GCMs) used in lieu of a statistically represented history. This technique will be used in this research to calculate projected ranges of regional climate for the key climatic variables that form the input to the agricultural production impact model. Among the previous studies, some used this technique to analyze the impacts of risk regarding climate change on irrigation water demand. This paper presents a more comprehensive analytical framework, including a new approach to determine the GCM model uncertainty. Copyright ASCE 2005.