Interrogating empirical-statistical downscaling

The delivery of downscaled climate information is increasingly seen as a vehicle of climate services, a driver for impacts studies and adaptation decisions, and for informing policy development. Empirical-statistical downscaling (ESD) is widely used; however, the accompanying responsibility is significant, and predicated on effective understanding of the limitations and capabilities of ESD methods. There remain substantial contradictions, uncertainties, and sensitivity to assumptions between the different methods commonly used. Yet providing decision-relevant downscaled climate projections to help support national and local adaptation is core to the growing global momentum seeking to operationalize what is, in effect, still foundational research. We argue that any downscaled climate information must address the criteria of being plausible, defensible and actionable. Climate scientists cannot absolve themselves of their ethical responsibility when informing adaptation and must, therefore, be diligent in ensuring any information provided adequately addresses these three criteria. Frameworks for supporting such assessment are not well developed. We interrogate the conceptual foundations of statistical downscaling methodologies and their assumptions, and articulate a framework for evaluating and integrating downscaling output into the wider landscape of climate information. For ESD there are key criteria that need to be satisfied to underpin the credibility of the derived product. Assessing these criteria requires the use of appropriate metrics to test the comprehensive treatment of local climate response to large-scale forcing, and to compare across methods. We illustrate the potential consequences of methodological choices on the interpretation of downscaling results and explore the purposes, benefits and limitations of using statistical downscaling.