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ABSTRACT The asynchronous regional regression model ( ARRM) is a flexible and computationally efficient statistical model that can downscale station-based or gridded daily values of any variable that can be transformed into an approximately symmetric distribution and for which a large-scale predictor exists. This technique was developed to bridge the gap between large-scale outputs from atmosphere-ocean general circulation models ( AOGCMs) and the fine-scale output required for local and regional climate impact assessments. ARRM uses piecewise regression to quantify the relationship between observed and modelled quantiles and then downscale future projections. Here, we evaluate the performance of three successive versions of the model in downscaling daily minimum and maximum temperature and precipitation for 20 stations in North America from diverse climate zones. Using cross-validation to maximize the independent comparison period, historical downscaled simulations are evaluated relative to observations in terms of three different quantities: the probability distributions, giving a visual image of the skill of each model; root-mean-square errors; and bias in nine quantiles that represent both means and extremes. Successive versions of the model show improved accuracy in simulating extremes, where AOGCMs are often most biased and which are frequently the focus of impact studies. Overall, the quantile regression-based technique is shown to be efficient, robust, and highly generalizable across multiple variables, regions, and climate model inputs. [ABSTRACT FROM AUTHOR]

Abstract: Assessing regional impacts of climate change begins with development of climate projections at relevant temporal and spatial scales. Here, proven statistical downscaling methods are applied to relatively coarse-scale atmosphere–ocean general circulation model (AOGCM) output to improve the simulation and resolution of spatial and temporal variability in temperature and precipitation across the US Great Lakes region. The absolute magnitude of change expected over the coming century depends on the sensitivity of the climate system to human forcing and on the trajectory of anthropogenic greenhouse gas emissions. Annual temperatures in the region are projected to increase 1.4±0.6°C over the near-term (2010–2039), by 2.0±0.7°C under lower and 3±1°C under higher emissions by midcentury (2040–2069), and by 3±1°C under lower and 5.0±1.2°C under higher emissions by end-of-century (2070–2099), relative to the historical reference period 1961–1990. Simulations also highlight seasonal and geographical differences in warming, consistent with recent trends. Increases in winter and spring precipitation of up to 20% under lower and 30% under higher emissions are projected by end-of-century, while projections for summer and fall remain inconsistent. Competing effects of shifting precipitation and warmer temperatures suggest little change in Great Lake levels over much of the century until the end of the century, when net decreases are expected under higher emissions. Overall, these projections suggest the potential for considerable changes to climate in the US Great Lakes region; changes that could be mitigated by reducing global emissions to follow a lower as opposed to a higher emissions trajectory over the coming century. [Copyright &y& Elsevier]