This paper describes the use of the CliCrop model in the context of climate change general assessment modeling. The MIT Integrated Global System Model (IGSM) framework is a global integrated assessment modeling framework that uses emission predictions and economic outputs from the MIT Emission Prediction and Policy Analysis (EPPA) model and earth system modeling predictions from the IGSM to drive a land system component, a crop model (CliCrop) and a Water Resource System (WRS) model. The global Agriculture and Water System are dependant upon and interlinked with the global climate system. As irrigated agriculture provides 60% of grains and 40% of all crop production on 20% of global crop lands and accounts for 80% of global water consumption, it is crucial that the agricultural-water linkage be properly modeled. Crop models are used to predict future yields, irrigation demand and to understand the effect of crop and soil type on food productivity and soil fertility. In the context of an integrated global assessment, a crop water-stress and irrigation demand model must meet certain specifications that are different for other crop models; it needs to be global, fast and generic with a minimal set of inputs. This paper describes how CliCrop models the physical and biological processes of crop growth and yield production and its use within the MIT Integrated Global System Model (IGSM) framework, including the data inputs. This paper discusses the global data bases used as input to CliCrop and provides a comparison of the accuracy of CliCrop with the detailed biological-based crop model DSSAT as well as with measured crop yields over the U.S. at the country level using reanalyzed weather data. In both cases CliCrop performed well and the analysis validated its use for climate change impact assessment. We then show why correctly modeling the soil is important for irrigation demand calculation, especially in temperate areas. Finally, we discuss a method to estimate actual water withdrawal from modeled physical crop requirements using U.S. historical data.