Despite the growing attention paid to the potential for climate change to affect climate variability and extremes, most models being used to assess climate change impacts on agricultural producers are based on averaged or aggregated data over relatively large populations. The only sense in which such studies can assess vulnerability – defined as the risk of a negative impact – is in terms of average impacts on groups of farm households stratified by some criterion such as a spatial unit, typically a political unit or an agro-ecozone.  In this presentation we present a new economic simulation approach that combines available historical data, statistical models, down-scaled climate data, experimental data and process-based production models, to approximate future distributions of production and associated economic outcomes such as farm net returns. We also show how this approach can be used to parameterize an economic impact assessment model, the Tradeoff Analysis Model for Multi-Dimensional Impact Assessment (TOA-MD; Antle 2011; Antle and Valdivia 2011). TOA-MD provides the capability to go beyond the analysis of averaged or aggregated data, by representing the distributions of economic, environmental and social outcomes in heterogeneous populations of farm households. When used for climate impact assessment, the TOA-MD model can be used to show how the distributions of outcomes are affected by climate and by adaptations farmers may undertake in response to climate change.