Regional Climate Models (RCMs) offer enhanced spatial resolution and a more realistic depiction of local climate processes. However, they often inherit systematic biases from their driving Global Climate Models (GCMs), which can compromise the accuracy of downscaled climate projections. To address this, bias correction techniques have been widely employed to adjust GCM and RCM outputs, particularly for climate impact and adaptation studies. Traditional methods, however, typically correct surface variables independently and lack physical and dynamical consistency. Bias correcting GCM boundary conditions prior to RCM simulation ensures a more coherent, physically and dynamically consistent, regional climate simulation with reduced errors. This study evaluates the effectiveness of such an approach using a calibration/validation framework, demonstrating significant error reduction during the validation (out-of-sample) period compared to uncorrected GCM data. We present an updated version of the open-source Python package, <em>Sub-Daily Multivariate Bias Correction (SDMBC) v2</em>, designed to correct RCM input variables using both reanalysis and raw GCM datasets. Enhancements include support for future climate projections, flexible horizontal and vertical interpolation for compatibility with diverse datasets, and a fully Python-based architecture optimized for parallel processing and high-performance computing. This paper illustrates the software's capabilities and provides a practical application example.