Zhou et al. (2026) A high-resolution dataset revealing the dynamical variations of the relative humidity in China

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-02-19
• Authors: Hongwu Zhou, Shan Ning, Ruizhe Yang, Sulaimon Habib Nazrollozoda, Zengyun Hu
• DOI: 10.1016/j.jhydrol.2026.135163

Research Groups

• School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
• Ministry of Education Key Laboratory for Coast and Island Development, Nanjing University, Nanjing 210023, China
• Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210023, China
• School of Marine Sciences and Engineering, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
• Institute of Veterinary Medicine of the Tajik Academy of Agricultural Sciences, 43, Kahhorov Street, 734005 Dushanbe, Tajikistan
• School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

Short Summary

This study developed a high-resolution (1 km × 1 km) daily relative humidity dataset for China (1951–2020) using Random Forest interpolation and analyzed its spatiotemporal variations, revealing a significant downward trend of −0.26% per decade from 1956 to 2020.

Objective

• To construct a high-resolution (1 km × 1 km) daily relative humidity dataset for China covering the period 1951–2020.
• To analyze the multiscale spatiotemporal variation characteristics of relative humidity in China, particularly from the perspective of agricultural spatial zoning.

Study Configuration

• Spatial Scale: China, with a spatial resolution of 1 km × 1 km.
• Temporal Scale: Daily, covering the period from 1951 to 2020.

Methodology and Data

• Models used: Random Forest (RF) spatial interpolation framework; Linear trend estimation; Mann-Kendall mutation test; Wavelet analysis.
• Data sources: Daily relative humidity data from ground observation stations in China. Covariates for interpolation included longitude, latitude, elevation, and spatial autocorrelation variables (spatial proximity values and distance).

Main Results

• The constructed dataset demonstrated high overall consistency with observation station data, with the DISO (Difference in Spatial Overlap) stabilizing around 0.5 after 1956.
• China's annual mean relative humidity showed a significant downward trend of −0.26% per decade from 1956 to 2020.
• The most pronounced decline in relative humidity was observed in the PHMYRR agricultural zone.
• Most agricultural zones experienced abrupt changes in relative humidity during the 1990s or early 21st century.
• Significant periodic variations in relative humidity were identified, with approximate periods of 7 years and 37 years.
• Regional variation in relative humidity was demarcated by 120°E, 45°N–95°E, 30°N, with larger decreases typically observed to the east of this line and relatively minor decreases to the west.

Contributions

• Provides a novel, high-resolution (1 km × 1 km daily) and long-term (1951–2020) relative humidity dataset for China, crucial for climate change impact studies.
• Offers scientific conclusions and theoretical guidance for understanding climate fluctuation processes and informing industrial and agricultural production activities in agricultural zones.
• Utilizes a robust Random Forest spatial interpolation framework that effectively captures nonlinear relationships and spatial dependencies by incorporating various covariates.

Funding

• Not explicitly stated in the provided text.

Citation

@article{Zhou2026highresolution,
  author = {Zhou, Hongwu and Ning, Shan and Yang, Ruizhe and Nazrollozoda, Sulaimon Habib and Hu, Zengyun},
  title = {A high-resolution dataset revealing the dynamical variations of the relative humidity in China},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135163},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135163}
}