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 predicting 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. For this reason, it is essential to understand how such bias adjustment algorithms work and what impact they can have on the underlying data. This bachelor thesis aims to find out and show how bias adjustment procedures work, how they can be implemented and applied in different programming languages, and what influence the application of such techniques can have on modeled temperature data for the region of Europe and its surroundings. This has been done by demonstrating and implementing five different bias adjustment procedures mathematically as well as in the programming languages Python and C++ and then applying different methods from the field of statistics in a detailed analysis to show the influence as well as the limitations of these techniques.