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Despite repeated calls for greater collaboration between physical and human geographers, the unique interdisciplinary potential of geography remains largely underutilized. Yet geographers are well positioned to take a leading role in the interdisciplinary turn in climate‐related research. This paper explores the possibilities for physical and human geographers to collaborate within and beyond the discipline, specifically on the topic of climate modeling. We first examine geographical research critically examining the production and circulation of climate knowledge. Drawing on insights from a recent literature called "Critical Physical Geography," we then outline how geographers might engage in collaborative and interdisciplinary work in order to promote more democratic practices of producing climate knowledge, enrich understandings of climate change, and more effectively serve goals of social and environmental justice. We argue that both the discipline of geography and the field of climate research stand to gain enormously from geographers' efforts to talk across the divide between social and natural science within and beyond the boundaries of geography. [ABSTRACT FROM AUTHOR]

Downscaling is a term that has been used to describe the range of methods that are used to infer regional-scale or local-scale climate information from coarsely resolved climate models. The use of statistical methods for this purpose is rooted in both operational weather forecasting and synoptic climatology and has become a widely applied method for development of regional climate change scenarios. This article provides an overview of statistical downscaling with a focus on assumptions, common predictors and predictands, and methodological approaches ranging from interpolation and scaling to regression-based methods, weather pattern-based techniques, and stochastic weather generators. Suggestions are made for improved assessment of the fundamental downscaling assumptions as well as reduction of uncertainty associated with application of downscaled climate information across models and greenhouse gas emission scenarios. [ABSTRACT FROM AUTHOR]