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1. Introduction
Climate impact assessments can be sensitive to biases in global climate model (GCM) output (IPCC 2013). For example, precipitation biases degrade hydrological simulations because of the nonlinear nature of runoff: a moderate amount of precipitation generates little runoff if the soil can absorb the moisture, while doubling the precipitation generates more than twice the runoff if the moisture storage capacity of the soil is exceeded. This nonlinear relationship becomes more extreme in arid regions (Wigley and Jones 1985). Similarly, temperature biases can influence the partition of precipitation into snow or rain, affecting the snowpack and therefore the timing and magnitude of runoff over the entire year.
For this reason, hydrological simulations generally use bias-corrected GCM output. Bias correction is often an integral part of downscaling GCM output (e.g., Wood et al. 2002; Maurer et al. 2010). Here, however, we consider the bias correction step alone. Bias correction is best applied on a spatial scale near the original GCM’s spatial resolution (Maraun 2013), so we examine bias correction on a grid commensurate with the original GCMs.
Many bias correction methods have been used in climate impact studies. One widely used method is quantile mapping (QM; e.g., Panofsky and Brier 1968; Wood et al. 2002; Thrasher et al. 2012), which adjusts a model value by mapping quantiles of the model’s distribution onto quantiles of the observations. QM has been applied to climate model output over both the United States (e.g., Maurer et al. 2007, 2014) and globally (Thrasher et al. 2012).
Previous studies have shown that QM alters the magnitude and even direction of mean changes projected from the original GCM (Hagemann et al. 2011; Pierce et al. 2013; Maurer and Pierce 2014). This can engender confusion and inconsistent results, for example, between bias-corrected GCM output for regional climate studies and unadulterated GCM output evaluated by the IPCC (2007, 2013). If a climate model has too much variability, QM tends to reduce variability on all time scales, including the trend (Pierce et al. 2013; Maurer and Pierce 2014). If the GCM has too little variability, QM tends to increase the trend. Since bias correction is a purely statistical method, it fails to discriminate between the physical processes determining trends associated with anthropogenic forcing and shorter-term...