Bias correction algorithms for modeled climate variables such as temperature, precipitation, and barometric pressure are used to approximate certain aspects of the distribution characteristics to the actual observed values. Thus, modeled climate data estimating future climate scenarios can be bias-adjusted using data from past periods so that climate variables and their distribution, as well as their variability, can be represented more realistically within the bias-adjusted time series. This document aims to introduce the command-line tool BiasAdjustCXX that enables the application of different scaling- and distribution-based bias adjustment techniques to minimize bias which can be estimated from time-series climate data.