A 25 km daily gridded dataset of meteorological variables and high-impact weather events for new-type power systems in China

Abstract

The new-type power system exhibits pronounced “weather dependency”, wherein high-impact weather events can significantly exacerbate operational security risks. A high-quality gridded dataset that involves both meteorological variables and high-impact weather events is of great significance for new-type power systems. In this study, a spatially adaptive optimal interpolation scheme is developed and applied to generate the China New-type Power Systems Meteorological (CNPS-Met) dataset. The CNPS-Met dataset covers the entire Chinese mainland, with a daily temporal resolution and a 25 km spatial resolution. It includes eight meteorological variables and eleven high-impact weather events, categorized from generation-side, grid-side and demand-side perspectives relevant to new-type power systems. Validation with existing datasets indicates that the CNPS-Met dataset generally exhibits superior performance in meteorological estimation. Specifically, the estimated mean relative errors for 2 m air temperature, 2 m specific humidity, 10 m wind speed, precipitation and surface pressure averaged over the Chinese mainland could be reduced by 1.7 %–18.5 %, 9.0 %–29.6 %, 1.9 %–8.5 %, 2.7 %–18 % and 4.9 %–5.2 %, respectively. On this basis, a series of high-impact weather events critical to the operation of new-type power system are identified. The spatial distribution of their frequency hotspots and intensity extremes are further analyzed. The CNPS-Met dataset (https://doi.org/10.12072/ncdc.nieer.db6972.2025, Zhang et al., 2025) is expected to benefit research and applications at the intersection of meteorology and new-type power systems.