A 0.1° monthly potential evapotranspiration dataset based on the optimal models over global vegetation zones

Abstract Choosing effective models for accurately estimating potential evapotranspiration (PET) is essential in various fields, including climatology, ecology, hydrology, and agronomy. However, many currently dominant PET products rely on multiple models with default parameters, which can introduce uncertainties into PET-related research. To address this issue, we derived the parameters for five widely used PET models using observations from 124 eddy covariance sites worldwide. By comparing their performances across various biomes, we identified the calibrated Priestley-Taylor and Milly-Dunne models as the optimal choices, capable of being applied effectively beyond their original observation sites. Using these optimal models, along with four widely-used meteorological datasets and annual land use and land cover data, we generated a monthly PET dataset at a 0.1° resolution during 1992–2022 across global vegetation zones. Finally, we compared the new dataset with the Global Land Evaporation Amsterdam Model V4.2a PET, including daily comparisons for each biome and annual trend comparisons during 1992–2022. This new PET dataset serves as an alternative resource for conducting PET-related research.