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The Global Change Assessment Model (GCAM) is a global integrated assessmentmodel used to project future societal and environmental scenarios, based oneconomic modeling and on a detailed representation of food and energyproduction systems. The terrestrial module in GCAM represents agriculturalactivities and ecosystems dynamics at the subregional scale, and must bedownscaled to be used for impact assessments in gridded models (e.g., climatemodels). In this study, we present the downscaling algorithm of the GCAMmodel, which generates gridded time series of global land use and land cover(LULC) from any GCAM scenario. The downscaling is based on a number ofuser-defined rules and drivers, including transition priorities (e.g., cropexpansion preferentially into grasslands rather than forests) and spatialconstraints (e.g., nutrient availability). The default parameterization isevaluated using historical LULC change data, and a sensitivity experimentprovides insights on the most critical parameters and how their influencechanges regionally and in time. Finally, a reference scenario and a climatemitigation scenario are downscaled to illustrate the gridded land useoutcomes of different policies on agricultural expansion and forestmanagement. Several features of the downscaling can be modified by providingnew input data or changing the parameterization, without any edits to thecode. Those features include spatial resolution as well as the number andtype of land classes being downscaled, thereby providing flexibility toadapt GCAM LULC scenarios to the requirements of a wide range of models andapplications. The downscaling system is version controlled and freelyavailable

Downscaling land use and land cover from the Global Change Assessment Model for coupling with Earth system models