Bias corrections of ERA5 and ERA5-land temperature using automatic weather station data in the Higher Central Himalaya: implications for hydro-meteorological and glaciological research

Dokriani Glacier Catchment (DGC), Central Himalaya. This study evaluates and corrects biases of ERA5 and ERA5-Land (ERA5L) mean temperature (T MEAN ) data for the DGC, using high-resolution daily observations from three Automatic Weather Stations (AWSs) in distinct settings: glacierized, proglacial, and forested. Five methods including Delta Change (DC), Linear Regression (LR), Empirical Quantile Mapping (EQM), Quantile Delta Mapping (QDM), and Generalized Additive Models (GAM) were used to identify the most effective method for correcting reanalysis data based on AWS observations at daily, monthly, and seasonal timescales (2011–2014) using bias, Root Mean Square Error (RMSE), correlation coefficient, and coefficient of determination. LR and GAM were the most effective, reducing biases to near zero and RMSE by up to 86 % at the seasonal scale, enabling more reliable climate-driven hydrological modeling, glaciological studies, and water-resource management in this monsoon-influenced region. Seasonal drivers can differentially influence dataset-specific corrections, with ERA5L showing substantial reductions in RMSE during monsoon periods. Hydrological models incorporating such improvements provide vital information for downstream river systems that are critical for South Asian livelihoods, agriculture, and hydropower. In contrast, ERA5 showed slight improvements, with biases that were significantly dependent on grid size; finer resolutions resulted in better error reduction. The validation based on 2014–2015 data confirmed that the LR and GAM methods effectively minimized the errors in the reanalysis dataset. • For the first time, ERA5 and ERA5-Land data were assessed for temperature bias. • GAM and LR reduce biases to near zero in Himalayan catchment. • Up to 86 % RMSE improvement can enhances hydrological modeling accuracy. • Seasonal bias corrections are vital for monsoon-driven glacier melt studies. • ERA5-Land must be prioritized in glacio-hydrological and climate impact studies.