Evaluation and statistical bias correction of ERA5-Land meteorological variables for a humid river basin in Southwest China.

High-quality meteorological data are essential for climate monitoring and renewable energy applications. ERA5-Land, a newly released high-resolution reanalysis dataset, provides a wide range of meteorological variables, but its accuracy remains a concern. This study evaluated the performance of ERA5-Land in the Lower Jinsha River Basin, the largest clean energy base in China, focusing on precipitation, wind speed, air temperature, and solar radiation. A statistical bias correction procedure was developed, combining month-specific regression fitting with daily and hourly adjustments. Results indicated that air temperature estimates agreed best with ground observations, with a coefficient of determination (R2) exceeding 0.87 and percent bias (Pbias) below 15%, followed by solar radiation. Precipitation and wind speed, in contrast, exhibited larger uncertainties (R2 < 0.31, Pbias up to 67.76%). After applying the statistical bias correction, systematic biases were largely eliminated across all examined variables. Absolute errors decreased by more than 10%, and temporal consistency also improved moderately, especially for wind speed and solar radiation, where R2 increased by 29.5% and 25.8%, respectively. The corrected dataset captured basin-wide climatic variations from 1980 to 2019, including decreasing precipitation, increasing temperature and solar radiation, and the spatial heterogeneity changes in wind speed. Overall, this study contributes to better knowledge of ERA5-Land uncertainties in multiple meteorological variables and provides a practical statistical correction framework, which can serve as a reference for data-scarce regions with similar climatic and geographical conditions and clean energy development contexts.