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Rain-season trends in precipitation and their effect in different climate regions of China during 1961–2008
Open accessAbstract
Using high-quality precipitation data from 524 stations, the trends of a set of precipitation variables during the main rain season (May–September) from 1961 to 2008 for different climate regions in China were analysed. However, different characteristics were displayed in different regions of China. In most temperate monsoon regions (north-eastern China), total rain-season precipitation and precipitation days showed decreasing trends; positive tendencies in precipitation intensity were, however, noted for most stations in this region. It is suggested that the decrease in rain-season precipitation is mainly related to there being fewer rain days and a change towards drier conditions in north-eastern China, and as a result, the available water resources have been negatively affected in the temperate monsoon regions. In most subtropical and tropical monsoon climate regions (south-eastern China), the rain-season precipitation and precipitation days (11–50, with > 50 mm) showed slightly positive trends. However, precipitation days with ≤ 10 mm decreased in these regions. Changes towards wetter conditions in this area, together with more frequent heavy rainfall events causing floods, have a severe impact on peoples' lives and socio-economic development. In general, the rain-season precipitation, precipitation days and rain-season precipitation intensity had all increased in the temperate continental and plateau/mountain regions of western China. This increase in rain-season precipitation has been favourable to pasture growth.
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“This performance pattern reflects the greater algorithmic challenges posed by complex terrain and heterogeneous precipitation mechanisms prevalent in these regions [74,77,78].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [74]
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“This consistently strong performance may be attributed to the dominant heavy rainfall mechanisms in these regions, which are primarily driven by large-scale synoptic systems [74][75][76].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [74]
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Research works printed but not linked: 40
- 10.1016/j.gloplacha.2004.06.001 · DOI
- Chen L X, 1991, Q. J. Appl. Meteorol., 2, 164
- Chen Y, 2005, J. Nat. Resource, 20, 637
- Ding Y H, 1994, The Asian Monsoon.
- Ding Y H Sun Y, 2003, (ISCC): Proc. Int. Symp. on Climate Change
- 10.1002/joc.1759 · DOI
- 10.1002/joc.1615 · DOI
- Domroes M, 1988, The Climate of China, 361
- 10.1126/science.289.5487.2068 · DOI
- Aquastat FAO’s Information System on Water and Agricultural
- Fu Q L, 2008, J. Trop. Meteorol., 24, 367
- 10.1029/2001GL014523 · DOI
- 2007, Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change
- 10.1007/BF02837477 · DOI
- 10.1002/joc.625 · DOI
- Li C Y, 2009, The Study of Meteorological Disasters and Chinese Response, 33
- 10.1016/j.jhydrol.2009.08.007 · DOI
- 10.1080/02508060108686913 · DOI
- 10.1002/hyp.7609 · DOI
- The Impact of Climate in 2006, 45
- The Impact of Climate in 2008, 36
- 10.1007/s00382-003-0319-3 · DOI
- Ren G Y, 2000, Q. J. Appl. Meteorol., 11, 322
- Shen D, 2010, Curr. Sci., 98, 1063
- Sun Y, 2009, Sci. China Ser., 39, 1487
- 10.1016/j.gloplacha.2007.03.009 · DOI
- 10.1023/A:1005488920935 · DOI · Paper page
- Wang D J, 2006, J. Trop. Meteorol., 3, 3
- Wang S W, 2000, Acta Geograph. Sin., 55, 281
- Wang S W, 1981, Geophys. J., 52, 117
- 10.1007/s11434-006-8204-1 · DOI
- Xu G C, 1992, Acta Meteorol. Sin., 50, 378
- 10.1016/j.jaridenv.2009.08.014 · DOI
- Ye D X, 2009, Resources Environ. Yangtze Basin, 18, 296
- Ye D Z, 1979, Meteorology of Qinghai-Xizang (Tibet) Plateau, 45
- 10.1016/S0022-1694(01)00594-7 · DOI
- 10.1175/JCLI-3318.1 · DOI
- Zhai P M, 1999, Acta Meteorol. Sin., 13, 278
- 10.1002/hyp.7599 · DOI
- Zhao Z C, 2005, Acta Meteorol. Sin., 19, 389
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A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data
Sun, H., Zhou, Q., Shi, L., Li, C., Qin, S., Yao, D., Xu, M., Huang, Y., Hu, Q., & Guan, Y. (2025) · Remote Sensing · doi:10.3390/rs17243976 · Cited by 4
Metadata
- Authors
- Song, Y., Achberger, C., & Linderholm, H. W.
- Affiliations
- China Meteorological Administration (CN)
- University of Gothenburg (SE)
- Venue
- Environmental Research Letters
- Publisher
- IOP Publishing
- Published
- 2011-07-01
- Volume
- 6
- Issue
- 3
- Pages
- 034025
- ISSN
- 1748-9326 (ISSN-L)
- OpenAlex
- W2162462697
- Cited by
- 102
- References
- 0
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 12 · 2023: 11 · 2022: 8 · 2021: 13 · 2020: 10 · 2019: 4 · 2018: 8 · 2017: 1 · 2016: 5 · 2015: 3 · 2014: 4 · 2013: 4 · 2012: 1
- FWCI
- 2.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Environmental Research Letters (publishedVersion, cc-by)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Styrelsen för Internationellt Utvecklingssamarbete (SWE-2009-245)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Keywords
- rainy days, precipitation intensity, temperate monsoon climate, subtropical monsoon climate, tropical monsoon climate, heavy rainfall, water resources, pasture growth
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