@Article{Zeng2026,
author="Zeng, Rongchang
and Xu, Jintao
and Chen, Tao
and Lin, Dawei
and Zhu, Xuchao
and Teng, Hongfen
and Yu, Wu
and Fu, Zhiyi
and Du, Yuanyuan
and Chang, Jinfeng
and Shi, Zhou
and Chen, Songchao",
title="China rainfall erosivity (2015--2023): A new annual dataset inferred from national hourly meteorological station records",
journal="Scientific Data",
year="2026",
month="Aug",
day="17",
abstract="Rainfall erosivity (R-factor) is a critical parameter for soil loss prediction and ecological risks assessment, yet its quantification is often limited by the coarse temporal resolution of available precipitation data. While high-frequency observations provide a more accurate characterization of rainfall kinetic energy, a national-scale assessment of the uncertainties induced by these varying data resolutions in China has been lacking. This study presents a new, high-precision annual rainfall erosivity dataset for China (2015--2023) derived from hourly precipitation records across 57,740 meteorological stations (at least 5 years). Using the hourly-derived erosivity (Rhourly) as the benchmark, we systematically evaluated the estimation biases of widely used daily and monthly empirical models. Results show that Rhourly exhibits a decreasing gradient from southeastern to northwestern China, peaking at over 8,000 MJ mm ha−1 h−1 y−1 in the Southern Red Soil Hilly Region. Nationwide, daily-scale estimates show a mean absolute error of 20.7{\%}, transitioning from systematic underestimation in the southeast to severe overestimation in the northwest. Monthly-scale estimations yield a higher mean absolute error of 25.8{\%}, with intensified underestimation in southern regions and a reversal of error direction in the northwest. Across China, the interannual coefficient of variation averages 59.0{\%}, with the highest variability (93.8{\%}) observed in the Northern Wind-Sand Region. By providing station-based benchmark data and identifying spatial error patterns, this dataset offers a robust foundation for high-precision soil erosion mapping, regional conservation planning, and climate change impact assessments.",
issn="2052-4463",
doi="10.1038/s41597-026-07995-6",
url="https://doi.org/10.1038/s41597-026-07995-6"
}