Challenges in reliable bias correction of hydro-climatic and energy data: Towards a reconstruction of datasets

• Evaluation of 18 bias correction methods across climatic, hydrological, and radiative variables. • Comparison of statistical, distribution-based, and machine learning approaches under diverse climatic conditions. • Multi-scale evaluation using complementary performance metrics and consistency with extremes and temporal cycles. • Analysis of calibration length, temporal sequencing, and iterative corrections. • Practical recommendations for context-specific bias correction applications.