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Statistical downscaling is a commonly used technique for translatinglarge-scale climate model output to a scale appropriate for assessingimpacts. To ensure downscaled meteorology can be used in climate impactstudies, downscaling must correct biases in the large-scale signal. A simpleand generally effective method for accommodating systematic biases inlarge-scale model output is quantile mapping, which has been applied to manyvariables and shown to reduce biases on average, even in the presence ofnon-stationarity. Quantile-mapping bias correction has been applied atspatial scales ranging from  hundreds of kilometers to individualpoints, such as weather station locations. Since water resources and othermodels used to simulate climate impacts are sensitive to biases in inputmeteorology, there is a motivation to apply bias correction at a scale fineenough that the downscaled data closely resemble historically observeddata, though past work has identified undesirable consequences to applyingquantile mapping at too fine a scale. This study explores the role of thespatial scale at which the quantile-mapping bias correction is applied, inthe context of estimating high and low daily streamflows across the westernUnited States. We vary the spatial scale at which quantile-mapping biascorrection is performed from 2° ( ∼  200 km) to1∕8° ( ∼  12 km) within a statistical downscalingprocedure, and use the downscaled daily precipitation and temperature todrive a hydrology model. We find that little additional benefit is obtained,and some skill is degraded, when using quantile mapping at scales finer thanapproximately 0.5° ( ∼  50 km). This can provideguidance to those applying the quantile-mapping bias correction method forhydrologic impacts analysis

Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies