Reanalysis data have proven to be a valuable support for hydrologic modeling and calculation of standardized climate indices, useful tools for characterizing local climate regimes and improving water resource management in areas with limited availability of observational data. Previous investigations, e.g. Arnone et al. (2018), Tarek et al. (2020), and Ahmed et al. (2024) reported several case studies on the use of reanalysis data, produced by the ECMWF, in hydrological applications. It has been demonstrated that the application of various bias correction techniques enhances the quality of reanalysis data, as highlighted by studies from Smitha et al. (2018), Francipane et al. (2023), and Teutschbein and Seibert (2012). This study examines the use of ERA5 reanalysis dataset, with its spatial resolution of approximately 31 km, in hydrological applications, emphasizing the critical role of bias correction (BS) techniques to improve
data applicability and understand their limitations through a case study in Georgia, where observational climate data is scarce. Specifically, this work aims to compare five different bias correction techniques to identify the most effective method for improving raw reanalysis.
