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For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensem-ble simulations provided by global and regional climate models, which are then downscaled and bias-adjusted for eachstudy site. For bias adjustment, different statistical methods that re-scaleclimate model outputs have been suggested inthe scientific literature. They range from simple univariate methods that adjust each meteorological variable individ-ually, to more complex and more demanding multivariate methods that take existing relationships between meteoro-logical variables into consideration. Over the past decade, several attempts have been made to evaluate such methodsin various regions. There is, however, still no guidance for choosing appropriate bias adjustment methods for a study athand. In particular, the question whether the benefits of potentially improved adjustments outweigh the cost of in-creased complexity, remains unanswered.This paper presents a comprehensive evaluation of the performance of two commonly used univariate and two multi-variate bias adjustment methods in reproducing numerous univariate, multivariate and temporal features of precipita-tion and temperature series in different catchments in Sweden. The paper culminates in a discussion on trade-offs between the potential benefits (i.e., skills and added value) and disadvantages (complexity and computational de-mand) of each method to offer plausible, defensible and actionable insights from the standpoint of climate-change im-pact studies in high latitudes.We concluded that all selected bias adjustment methods generally improved the raw climate model simulations, but that not a single method consistently outperformed the other methods. There were, however, differences in the methods' performance for particular statistical features, indicating that other practical aspects such as computationaltime and heavy theoretical requirements should also be taken into consideration when choosing an appropriate biasadjustment method

Summary A statistical bias correction methodology for global climate simulations is developed and applied to daily land precipitation and mean, minimum and maximum daily land temperatures. The bias correction is based on a fitted histogram equalization function. This function is defined daily, as opposed to earlier published versions in which they were derived yearly or seasonally at best, while conserving properties of robustness and eliminating unrealistic jumps at seasonal or monthly transitions. The methodology is tested using the newly available global dataset of observed hydrological forcing data of the last 50 years from the EU project WATCH (WATer and global CHange) and an initial conditions ensemble of simulations performed with the ECHAM5 global climate model for the same period. Bias corrections are derived from 1960 to 1969 observed and simulated data and then applied to 1990–1999 simulations. Results confirm the effectiveness of the methodology for all tested variables. Bias corrections are also derived using three other non-overlapping decades from 1970 to 1999 and all members of the ECHAM5 initial conditions ensemble. A methodology is proposed to use the resulting “ensemble of bias corrections” to quantify the error in simulated scenario projections of components of the hydrological cycle.