MDG625: a daily high-resolution meteorological dataset derived by a geopotential-guided attention network in Asia (1940–2023)

Abstract

The long-term and reliable meteorological reanalysis dataset with high spatial–temporal resolution is crucial for various hydrological and meteorological applications, especially in regions or periods with scarce in situ observations and with limited open-access data. Based on the fifth-generation reanalysis dataset (ERA5, produced by the European Centre for Medium-Range Weather Forecasts, 0.25°×0.25°, since 1940) and CLDAS (China Meteorological Administration Land Data Assimilation System, 0.0625°×0.0625°, since 2008), we propose a novel downscaling method Geopotential-guided Attention Network (GeoAN), leveraging the high spatial resolution of CLDAS and the extended historical coverage of ERA5, and produce the daily multi-variable (2 m temperature, surface pressure, and 10 m wind speed) meteorological dataset MDG625. MDG625 (0.0625° Meteorological Dataset derived by GeoAN) covers most of Asia from 0.125° S to 64.875° N and 60.125 to 160.125° E, and contains data starting in 1940. Compared with other downscaling methods, GeoAN shows better performance with R2 (2 m temperature, surface pressure, and 10 m wind speed reach 0.990, 0.998, and 0.781, respectively). MDG625 demonstrates superior continuity and consistency from both spatial and temporal perspectives. We anticipate that the GeoAN method and this dataset, MDG625, will aid in climate studies of Asia and will contribute to improving the accuracy of reanalysis products from the 1940s. The MDG625 dataset (Song et al., 2024) is presented at https://doi.org/10.57760/sciencedb.17408, and the code can be found at https://github.com/songzijiang/GeoAN (last access: 8 April 2025).