Climate change projections are studied using General Circulation Models (GCMs). GCMs are models that simulate climate on a broad scale, hence they cannot be directly used in local impact studies, such as, for example, hydrological studies. GCMs must go through a process of downscaling, to adjust their results in terms of spatial scale and reduce their bias before being used at the local scale. Quantile Mapping is one of the most widely used approaches for bias correcting GCM climate outputs. However, in its conventional formulation QM assumes a time-invariant correction function, which potentially results in additional biases. This has motivated the development of trend-preserving variations, accounting for a non-stationary correction function and aiming to preserve the raw GCM signal. Unfortunately, choosing which variation to use is not straight-forward. We present the climQMBC package (<a href="https://github.com/saedoquililongo/climQMBC" target="_blank" rel="noopener">https://github.com/saedoquililongo/climQMBC</a> or <a href="https://doi.org/10.5281/zenodo.18392900" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.18392900</a>) as an easy-to-use tool to compare quantile mapping approaches. climQMBC is available in Python, R and MATLAB, and contains the classic QM method and four trend-preserving variations: Detrended Quantile Mapping (DQM), Quantile Delta Mapping (QDM), Unbiased Quantile Mapping (UQM) and Scaled Distribution Mapping (SDM). This package has a built-in summary report that allows comparing methods in terms of their capability of preserving raw GCM trends. A synthetic exercise showed that the most reliable methods are the UQM and DQM.