ChinaMet: A High-resolution Multi-element Meteorological Driving Dataset for China via Multi-source Data Fusion

English name

CSTR

CSTR:11738.11.NCDC.NIEER.DB6722.2025

DOI

10.12072/ncdc.nieer.db6722.2025

Share type

Category

Meteorology

contributors

ZHANG Ling , HU yingyi , ZHAO Yanbo , Che Tao

Datasets description

ChinaMet is a high-resolution (1 km) and long-term (1980–2024) meteorological driving dataset for China, developed by fusing multi-source remote sensing data, reanalysis products, and observational data from over 2,000 meteorological stations. The dataset includes eight meteorological variables: precipitation (pre), mean 2-meter air temperature (tmpmean), maximum temperature (tmpmax), minimum temperature (tmpmin), 10-meter wind speed (wind), relative humidity (rhu), surface pressure (pres), and potential evapotranspiration (pet). The data span 1980–2024 with spatial resolutions of 0.1° and 0.01°, and temporal resolutions of annual/monthly/daily. Each file is named as ChinaMet_%S_%V_%T.nc”, ‘%S’ represents the data space resolution, ‘%V’ represents the variable name, and ‘%T’ represents the time, e.g., 1982_01_01.

Base information

collect time	1980/01/01 - 2024/12/31
collect place	China
data size	3.5 TiB
data format	nc4
Data time resolution	year
Coordinate system	WGS84

Data source description

Meteorological station observations were sourced from the China Meteorological Administration (CMA). Fused remote sensing precipitation products include IMERG, SM2RAIN-ASCAT, and CMORPH. Reanalysis data utilized the ERA5-Land product from the European Centre for Medium-Range Weather Forecasts (ECMWF). Additional remote sensing inputs include AVHRR NDVI and MODIS land surface temperature data.

Data processing method

The dataset was developed using knowledge-guided machine learning combined with climate-field-assisted spatial downscaling techniques. Potential evapotranspiration (PET) is provided in two versions: petHG (estimated via the Hargreaves method) and petPM (derived using the Penman-Monteith method).

Data quality description

The temporal and spatial resolution and accuracy of the data are better than those of similar products.

Project information

#	number	name	type
1	42271286		National Natural Science Foundation of China
2	2023454	2023454	other
3	23ZDKA0004	Comparative Study on Water and Ecological Changes in Inland River Basins	Major Science and Technology Projects in Gansu Province

Citations and annotations

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For the achievements published in English, please refer to the following specifications： The dataset is provided by National Cryosphere Desert Data Center. (http://www.ncdc.ac.cn).

Data reference

ZHANG Ling, HU yingyi, ZHAO Yanbo, Che Tao. ChinaMet: A High-resolution Multi-element Meteorological Driving Dataset for China via Multi-source Data Fusion. National Cryosphere Desert Data Center(http://www.ncdc.ac.cn), 2025. https://cstr.cn/CSTR:11738.11.NCDC.NIEER.DB6722.2025.
ZHANG Ling, HU yingyi, ZHAO Yanbo, Che Tao. ChinaMet: A High-resolution Multi-element Meteorological Driving Dataset for China via Multi-source Data Fusion. National Cryosphere Desert Data Center(http://www.ncdc.ac.cn), 2025. https://www.doi.org/10.12072/ncdc.nieer.db6722.2025.

EndnoteEN BibtexEN RISEN

Papers to be cited

Zhang Ling, Li Xin, Zheng Donghai, Zhang Kun, Ma Qimin, Zhao Yanbo, Ge Yingchun. Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach[J]. Journal of Hydrology, 2021, 594:125969.

Reference format： Endnote Bibtex RIS
Hu Yingyi, Zhang Ling. Added value of merging techniques in precipitation estimates relative to gauge-interpolation algorithms of varying complexity[J]. Journal of Hydrology, 2024, 645:132214. DOI: https://doi.org/10.1016/j.jhydrol.2024.132214.

Reference format： Endnote Bibtex RIS

license agreement

This work is licensed under CC BY 4.0 (Creative Commons Attribution 4.0 International License).

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#	title	file size
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2	ChinaMet

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publications

Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach

Added value of merging techniques in precipitation estimates relative to gauge-interpolation algorithms of varying complexity

Theme:
Relative humidity ground pressure evaporation multi-source data fusion
Subject:
Atmospheric Science
Time:
1980-2024
Place:
China

east

west

south

north

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Contact information

contributors:: ZHANG Ling , HU yingyi , ZHAO Yanbo , Che Tao
author:: ZHANG Ling
manager:: ZHANG Ling

Platform Service information

contacts:: Li Hongxing
phone:: 0931-4967592
mailbox:: ncdc@lzb.ac.cn