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Water resources are essential to the ecosystem and social economy in thedesert and oasis of the arid Tarim River basin, northwestern China, andexpected to be vulnerable to climate change. It has been demonstrated thatregional climate models (RCMs) provide more reliable results for a regionalimpact study of climate change (e.g., on water resources) than generalcirculation models (GCMs). However, due to their considerable bias it isstill necessary to apply bias correction before they are used for waterresources research. In this paper, after a sensitivity analysis on inputmeteorological variables based on the Sobol' method, we compared fiveprecipitation correction methods and three temperature correction methods indownscaling RCM simulations applied over the Kaidu River basin, one of theheadwaters of the Tarim River basin. Precipitation correction methodsapplied include linear scaling (LS), local intensity scaling (LOCI), powertransformation (PT), distribution mapping (DM) and quantile mapping (QM),while temperature correction methods are LS, variance scaling (VARI) and DM.The corrected precipitation and temperature were compared to the observedmeteorological data, prior to being used as meteorological inputs of adistributed hydrologic model to study their impacts on streamflow. Theresults show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCMsimulations are heavily biased from observed meteorological data, and itsuse for streamflow simulations results in large biases from observedstreamflow, and all bias correction methods effectively improved thesesimulations; (3) for precipitation, PT and QM methods performed equally bestin correcting the frequency-based indices (e.g., standard deviation,percentile values) while the LOCI method performed best in terms of thetime-series-based indices (e.g., Nash–Sutcliffe coefficient, <i>R</i><sup>2</sup>); (4)for temperature, all correction methods performed equally well in correctingraw temperature; and (5) for simulated streamflow, precipitation correctionmethods have more significant influence than temperature correction methodsand the performances of streamflow simulations are consistent with those ofcorrected precipitation; i.e., the PT and QM methods performed equally best incorrecting flow duration curve and peak flow while the LOCI method performedbest in terms of the time-series-based indices. The case study is for anarid area in China based on a specific RCM and hydrologic model, but themethodology and some results can be applied to other areas and models

Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China