Near‐Surface Atmospheric Pressure Trends and Their Relationship to Temperature Over China (1961–2023)

ABSTRACT Near‐surface atmospheric pressure (NSAP) is a key physical quantity characterising atmospheric circulation features and thermodynamic structure, yet the long‐term trend and its relationship with near‐surface air temperature (NSAT) changes in China remain unclear. This study, based on homogenised monthly observational data from 765 meteorological stations of the National Meteorological Information Center of the China Meteorological Administration from 1961 to 2023, systematically analyzes the spatiotemporal evolution characteristics of NSAP in China and quantifies its relationship to NSAT changes. The results indicate that from 1961 to 2023, against the backdrop of a significant increase in nationwide NSAT, 0.29°C decade −1 , the annual average NSAP in China shows a slight declining trend of −0.02 hPa decade −1 , though not statistically significant. Spatially, the trend in NSAP from southeast to northwest presents a “decrease–increase–decrease” pattern. Among these, regions with lower elevations such as Central China (C) and Southern China (S), as well as the Western Arid and Aemi‐arid region (WAS), mainly exhibit a declining trend, magnitude exceeding 0.09 hPa decade −1 . Conversely, significant increasing trends are observed in the Qinghai‐Tibet (QT) region and the eastern arid (EA) region, exceeding 0.1 hPa decade −1 . This result suggests that topography plays a crucial role in modulating NSAP changes under warming. On a seasonal scale, NSAP changes exhibit noticeable asymmetry. The national average NSAP shows a declining trend during spring and autumn, while it increases during winter and summer. The QT region, as a unique geographical unit, displays an increasing trend in NSAP across all seasons. Correlation analysis reveals that, across China, 67.2% of the monitoring stations have observed a negative correlation between NSAP and NSAT (maximum negative correlation coefficient reaching −0.77). However, in the QT and EA, the relationship between the NSAP and NSAT primarily exhibits a positive correlation. Further analysis of the trend‐based ratio (ΔP/ΔT) reveals seasonally asymmetric values across China, with positive values in winter and summer, and negative values in spring and autumn. The strongest negative trend‐based ratio is observed in autumn (−0.33 hPa °C −1 ), while the strongest positive trend‐based ratio is observed in winter (0.21 hPa °C −1 ). These findings provide new observational benchmarks for understanding the dynamic processes underlying regional climate change in China.

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Publication Details

Journal
International Journal of Climatology
Published
2026-09-18
DOI
https://doi.org/10.1002/joc.70592
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

Near‐Surface Atmospheric Pressure Trends and Their Relationship to Temperature Over China (1961–2023)

Siyan Dong, Robert V. Rohli, Xiaodong Yan, Lijuan Cao et al.
International Journal of Climatology
Climate variability and models
article

Near‐Surface Atmospheric Pressure Trends and Their Relationship to Temperature Over China (1961–2023)

Siyan Dong, Robert V. Rohli, Xiaodong Yan, Lijuan Cao, Leibin Wang, Qigen Lin, Yuan Fang, Lifan Chen
article en

Abstract

ABSTRACT Near‐surface atmospheric pressure (NSAP) is a key physical quantity characterising atmospheric circulation features and thermodynamic structure, yet the long‐term trend and its relationship with near‐surface air temperature (NSAT) changes in China remain unclear. This study, based on homogenised monthly observational data from 765 meteorological stations of the National Meteorological Information Center of the China Meteorological Administration from 1961 to 2023, systematically analyzes the spatiotemporal evolution characteristics of NSAP in China and quantifies its relationship to NSAT changes. The results indicate that from 1961 to 2023, against the backdrop of a significant increase in nationwide NSAT, 0.29°C decade −1 , the annual average NSAP in China shows a slight declining trend of −0.02 hPa decade −1 , though not statistically significant. Spatially, the trend in NSAP from southeast to northwest presents a “decrease–increase–decrease” pattern. Among these, regions with lower elevations such as Central China (C) and Southern China (S), as well as the Western Arid and Aemi‐arid region (WAS), mainly exhibit a declining trend, magnitude exceeding 0.09 hPa decade −1 . Conversely, significant increasing trends are observed in the Qinghai‐Tibet (QT) region and the eastern arid (EA) region, exceeding 0.1 hPa decade −1 . This result suggests that topography plays a crucial role in modulating NSAP changes under warming. On a seasonal scale, NSAP changes exhibit noticeable asymmetry. The national average NSAP shows a declining trend during spring and autumn, while it increases during winter and summer. The QT region, as a unique geographical unit, displays an increasing trend in NSAP across all seasons. Correlation analysis reveals that, across China, 67.2% of the monitoring stations have observed a negative correlation between NSAP and NSAT (maximum negative correlation coefficient reaching −0.77). However, in the QT and EA, the relationship between the NSAP and NSAT primarily exhibits a positive correlation. Further analysis of the trend‐based ratio (ΔP/ΔT) reveals seasonally asymmetric values across China, with positive values in winter and summer, and negative values in spring and autumn. The strongest negative trend‐based ratio is observed in autumn (−0.33 hPa °C −1 ), while the strongest positive trend‐based ratio is observed in winter (0.21 hPa °C −1 ). These findings provide new observational benchmarks for understanding the dynamic processes underlying regional climate change in China.

International Journal of Climatology
Louisiana State University (US), China Meteorological Administration (CN), Beijing Normal University (CN), Hebei Normal University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 14%
Climate variability and models
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