Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. Theclimatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodologicaldecisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climateprojections with ecological models have received little explicit attention. We review Global Climate Model (GCM)performance along different dimensions of change and compare frameworks for integrating GCM output into ecologicalmodels. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments onmean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from whatwe term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historicaland future time periods. This approach leverages the experimental framework used in climate modeling, in which historicalclimate simulations serve as controls for future projections. Moreover, it can capture projected changes in theintensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes.Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicableacross species and systems. We also highlight practical considerations for the selection of climate models and data products,emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size ofdownscaled climate model output. Our review illustrates how an understanding of how climate model outputs arederived and downscaled can improve the selection and application of climatic data used in ecological modeling.
