Effects of Intraseasonal Oscillation on Timing and Subseasonal Predictability of Mei-yu Onset over the Yangtze River Basin

Sizhuo Wei aKey Laboratory of Meteorological Disaster of Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

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Pang-Chi Hsu aKey Laboratory of Meteorological Disaster of Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

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Jinhui Xie aKey Laboratory of Meteorological Disaster of Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

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Abstract

The time of rainy season onset is crucial information for policymakers, especially in densely populated regions such as the Yangtze River basin (YRB) in China. In this study, we proposed a new grid-based index to objectively detect mei-yu onset timing using reanalysis data and model predictions, and then we identified the key processes via which intraseasonal oscillation (ISO) affects the YRB mei-yu onset and its subseasonal predictability based on scale-decomposed moisture analysis. Climatologically, propagation of an ISO anticyclonic anomaly toward East China supports the moisture convergence required for rainy season onset over the YRB via interaction with the seasonal-mean moisture component. In the years of early mei-yu onset, the ISO was enhanced earlier in May and favored the moisture convergence anomaly in late May–early June, when the mei-yu started. In contrast, the enhanced ISO and associated moistening processes were observed later in June–early July in the years with delayed onset. The European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction models show skillful prediction of mei-yu onset at forecast lead times of 5–6 pentads, whereas the China Meteorological Administration model has limited skill of 3 pentads. The differences in model prediction skill are related to the accuracy of predicted moisture convergence anomalies induced by the ISO. The prediction bias in mei-yu onset timing (early or delayed) is also connected to bias in the occurrence timing of enhanced intraseasonal perturbations, suggesting the vital role of ISO in YRB mei-yu onset on the subseasonal time scale.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Pang-Chi Hsu, pangchi@nuist.edu.cn

Abstract

The time of rainy season onset is crucial information for policymakers, especially in densely populated regions such as the Yangtze River basin (YRB) in China. In this study, we proposed a new grid-based index to objectively detect mei-yu onset timing using reanalysis data and model predictions, and then we identified the key processes via which intraseasonal oscillation (ISO) affects the YRB mei-yu onset and its subseasonal predictability based on scale-decomposed moisture analysis. Climatologically, propagation of an ISO anticyclonic anomaly toward East China supports the moisture convergence required for rainy season onset over the YRB via interaction with the seasonal-mean moisture component. In the years of early mei-yu onset, the ISO was enhanced earlier in May and favored the moisture convergence anomaly in late May–early June, when the mei-yu started. In contrast, the enhanced ISO and associated moistening processes were observed later in June–early July in the years with delayed onset. The European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction models show skillful prediction of mei-yu onset at forecast lead times of 5–6 pentads, whereas the China Meteorological Administration model has limited skill of 3 pentads. The differences in model prediction skill are related to the accuracy of predicted moisture convergence anomalies induced by the ISO. The prediction bias in mei-yu onset timing (early or delayed) is also connected to bias in the occurrence timing of enhanced intraseasonal perturbations, suggesting the vital role of ISO in YRB mei-yu onset on the subseasonal time scale.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Pang-Chi Hsu, pangchi@nuist.edu.cn
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  • Bombardi, R. J., J. L. Kinter III, and O. W. Frauenfeld, 2019: A global gridded dataset of the characteristics of the rainy and dry seasons. Bull. Amer. Meteor. Soc., 100, 13151328, https://doi.org/10.1175/BAMS-D-18-0177.1.

    • Search Google Scholar
    • Export Citation
  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. W. Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D4110, https://doi.org/10.1029/2007JD009132.

    • Search Google Scholar
    • Export Citation
  • China Meteorological Administration, 2017: Meiyu Monitoring Indices, GB/T 33671—2017 (in Chinese). General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, 10 pp.

  • de Andrade, F. M., C. A. S. Coelho, and I. F. A. Cavalcanti, 2019: Global precipitation hindcast quality assessment of the subseasonal to seasonal (S2S) prediction project models. Climate Dyn., 52, 54515475, https://doi.org/10.1007/s00382-018-4457-z.

    • Search Google Scholar
    • Export Citation
  • Deng, L., T. Li, J. Liu, and M. Peng, 2016: Factors controlling the interannual variations of MJO intensity. J. Meteor. Res., 30, 328340, https://doi.org/10.1007/s13351-016-5113-3.

    • Search Google Scholar
    • Export Citation
  • Ding, Y., 1992: Summer monsoon rainfalls in China. J. Meteor. Soc. Japan, 70, 373396, https://doi.org/10.2151/jmsj1965.70.1B_373.

  • Ding, Y., 2004: Seasonal march of the East Asian summer monsoon. East Asian Monsoon, C. P. Chang, Ed., World Scientific, 3–53.

  • Ding, Y., J.-J. Liu, Y. Sun, Y. Liu, J. He, and Y. Song, 2007: A study of the synoptic climatology of the Meiyu system in East Asia (in Chinese). Chin. J. Atmos. Sci., 31, 10821101, https://doi.org/10.3878/j.issn.1006-9895.2007.06.05.

    • Search Google Scholar
    • Export Citation
  • Ding, Y., P. Liang, Y. Liu, and Y. Zhang, 2020: Multiscale variability of Meiyu and its prediction: A new review. J. Geophys. Res. Atmos., 125, e2019JD031496, https://doi.org/10.1029/2019JD031496.

    • Search Google Scholar
    • Export Citation
  • Duchon, C. E., 1979: Lanczos filtering in one and two dimensions. J. Appl. Meteor., 18, 10161022, https://doi.org/10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ha, K.-J., S. Moon, A. Timmermann, and D. Kim, 2020: Future changes of summer monsoon characteristics and evaporative demand over Asia in CMIP6 simulations. Geophys. Res. Lett., 47, e2020GL087492, https://doi.org/10.1029/2020GL087492.

    • Search Google Scholar
    • Export Citation
  • He, B., Y. Zhang, T. Li, and W.-T. Hu, 2017: Interannual variability in the onset of the South China Sea summer monsoon from 1997 to 2014. Atmos. Oceanic Sci. Lett., 10, 7381, https://doi.org/10.1080/16742834.2017.1237853.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., J.-Y. Lee, and K.-J. Ha, 2016: Influence of boreal summer intraseasonal oscillation on rainfall extremes in southern China. Int. J. Climatol., 36, 14031412, https://doi.org/10.1002/joc.4433.

    • Search Google Scholar
    • Export Citation
  • Hu, D., A. Duan, Y. Tang, and W. Yu, 2023: Delayed onset of the tropical Asian summer monsoon in CMIP6 can be linked to the cold bias over the Tibetan Plateau. Environ. Res. Lett., 18, 114005, https://doi.org/10.1088/1748-9326/acff79.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., and Y. Ding, 2010: Simulation of 1991–2005 Meiyu seasons in the Yangtze-Huaihe region using BCC_RegCM 1.0. Chin. Sci. Bull., 55, 10771083, https://doi.org/10.1007/s11434-009-0473-z.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., Y.-H. Ding, and F. Liao, 2008: A study of updated definition and climatological characters of Meiyu season in the Yangtze-Huaihe region (in Chinese). Chin. J. Atmos. Sci., 32, 101112, https://doi.org/10.3878/j.issn.1006-9895.2008.01.09.

    • Search Google Scholar
    • Export Citation
  • Huang, Q., L. Wang, J. He, and Z. Guan, 2010: A review of studies on the Meiyu season in the Jiang-Huai region (in Chinese). J. Zhejiang Meteor., 31, 27, https://doi.org/10.16000/j.cnki.zjqx.2010.02.002.

    • Search Google Scholar
    • Export Citation
  • Huang, Z., W. Zhang, X. Geng, and P.-C. Hsu, 2020: Accumulated effect of intra-seasonal oscillation convections over the tropical western North Pacific on the meridional location of western Pacific subtropical high. Front. Earth Sci., 8, 579442, https://doi.org/10.3389/feart.2020.579442.

    • Search Google Scholar
    • Export Citation
  • Jin, R., W. Li, B. Zhang, and C. Yan, 2012: A study of the relationship between East Asia subtropical westerly jet and abnormal Meiyu in the middle-lower reaches of the Yangtze River (in Chinese). Chin. J. Atmos. Sci., 36, 722732, https://doi.org/10.3878/j.issn.1006-9895.2011.11157.

    • Search Google Scholar
    • Export Citation
  • Lee, J.-Y., and B. Wang, 2014: Future change of global monsoon in the CMIP5. Climate Dyn., 42, 101119, https://doi.org/10.1007/s00382-012-1564-0.

    • Search Google Scholar
    • Export Citation
  • Lei, X., M. Song, and S. Zhang, 2022: Association between summer activity characteristic indices of the South Asia high and the West Pacific subtropical high and precipitation distribution in eastern China (in Chinese). Plateau Meteor., 41, 489501, https://doi.org/10.7522/j.issn.1000-0534.2021.00099.

    • Search Google Scholar
    • Export Citation
  • Li, H., S. He, K. Fan, and H. Wang, 2019: Relationship between the onset date of the Meiyu and the South Asian anticyclone in April and the related mechanisms. Climate Dyn., 52, 209226, https://doi.org/10.1007/s00382-018-4131-5.

    • Search Google Scholar
    • Export Citation
  • Li, J., B. Liu, and J. Mao, 2021: Climatological intraseasonal oscillation in the middle–upper troposphere and its effect on the northward migration of the East Asian westerly jet and rain belt over eastern China. Int. J. Climatol., 41, 50845099, https://doi.org/10.1002/joc.7118.

    • Search Google Scholar
    • Export Citation
  • Li, W., H.-C. Ren, J. Zuo, and H.-L. Ren, 2018: Early summer southern China rainfall variability and its oceanic drivers. Climate Dyn., 50, 46914705, https://doi.org/10.1007/s00382-017-3898-0.

    • Search Google Scholar
    • Export Citation
  • Li, X., and R. Lu, 2017: Extratropical factors affecting the variability in summer precipitation over the Yangtze River Basin, China. J. Climate, 30, 83578374, https://doi.org/10.1175/JCLI-D-16-0282.1.

    • Search Google Scholar
    • Export Citation
  • Liang, P., and Y. Ding, 2012: Climatologic characteristics of the intraseasonal oscillation of East Asian Meiyu (in Chinese). Acta Meteor. Sin., 70, 418435, https://doi.org/10.11676/qxxb2012.036.

    • Search Google Scholar
    • Export Citation
  • Liang, P., Z.-Z. Hu, Y. Liu, X. Yuan, X. Li, and X. Jiang, 2019: Challenges in predicting and simulating summer rainfall in the eastern China. Climate Dyn., 52, 22172233, https://doi.org/10.1007/s00382-018-4256-6.

    • Search Google Scholar
    • Export Citation
  • Liang, P., Z.-Z. Hu, Y. Ding, and Q. Qian, 2021: The extreme Mei-Yu season in 2020: Role of MJO and cooperative influence of the Pacific and Indian Oceans. Adv. Atmos. Sci., 38, 20402054, https://doi.org/10.1007/s00376-021-1078-y.

    • Search Google Scholar
    • Export Citation
  • Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277.

    • Search Google Scholar
    • Export Citation
  • Liu, B., Y. Yan, C. Zhu, S. Ma, and J. Li, 2020: Record‐breaking Meiyu rainfall around the Yangtze River in 2020 regulated by the subseasonal phase transition of the North Atlantic Oscillation. Geophys. Res. Lett., 47, e2020GL090342, https://doi.org/10.1029/2020GL090342.

    • Search Google Scholar
    • Export Citation
  • Liu, L., X. Fu, M. Lin, B. Yin, Z. Liao, and F. Shen, 2010: Study on forecast signal of Meiyu onset in Jiang-Huai region (in Chinese). Meteor. Hydrol. Mar. Instrum., 27, 6671.

    • Search Google Scholar
    • Export Citation
  • Liu, M., R. Hang, B. Zhang, and X. Jin, 2013: Influence of subtropical high’s variation period and structure on plum rain onset (in Chinese). J. Meteor. Sci., 33, 430435, https://doi.org/10.3969/2012jms.0166.

    • Search Google Scholar
    • Export Citation
  • Martin, G. M., A. Chevuturi, R. E. Comer, N. J. Dunstone, A. A. Scaife, and D. Zhang, 2019: Predictability of South China Sea summer monsoon onset. Adv. Atmos. Sci., 36, 253260, https://doi.org/10.1007/s00376-018-8100-z.

    • Search Google Scholar
    • Export Citation
  • Martin, G. M., N. J. Dunstone, A. A. Scaife, and P. E. Bett, 2020: Predicting June mean rainfall in the middle/lower Yangtze River Basin. Adv. Atmos. Sci., 37, 2941, https://doi.org/10.1007/s00376-019-9051-8.

    • Search Google Scholar
    • Export Citation
  • Qi, Y., T. Li, R. Zhang, and Y. Chen, 2019: Interannual relationship between intensity of rainfall intraseasonal oscillation and summer-mean rainfall over Yangtze River Basin in eastern China. Climate Dyn., 53, 30893108, https://doi.org/10.1007/s00382-019-04680-w.

    • Search Google Scholar
    • Export Citation
  • Qian, Y., P. Hsu, Z. Fu, Y. Liu, and Q. Li, 2022: Decadal change of Meiyu onset over Yangtze River and its causes. Sustainability, 14, 5085, https://doi.org/10.3390/su14095085.

    • Search Google Scholar
    • Export Citation
  • Qiao, S., and Coauthors, 2021: The longest 2020 Meiyu season over the past 60 years: Subseasonal perspective and its predictions. Geophys. Res. Lett., 48, e2021GL093596, https://doi.org/10.1029/2021GL093596.

    • Search Google Scholar
    • Export Citation
  • Sampe, T., and S.-P. Xie, 2010: Large-scale dynamics of the Meiyu-Baiu rainband: Environmental forcing by the westerly jet. J. Climate, 23, 113134, https://doi.org/10.1175/2009JCLI3128.1.

    • Search Google Scholar
    • Export Citation
  • Sperber, K. R., H. Annamalai, I.-S. Kang, A. Kitoh, A. Moise, A. Turner, B. Wang, and T. Zhou, 2013: The Asian summer monsoon: An intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Climate Dyn., 41, 27112744, https://doi.org/10.1007/s00382-012-1607-6.

    • Search Google Scholar
    • Export Citation
  • Song, Z., C. Zhu, J. Su, and B. Liu, 2016: Coupling modes of climatological intraseasonal oscillation in the East Asian summer monsoon. J. Climate, 29, 63636382, https://doi.org/10.1175/JCLI-D-15-0794.1.

    • Search Google Scholar
    • Export Citation
  • Su, Q., R. Lu, and C. Li, 2014: Large-scale circulation anomalies associated with interannual variation in monthly rainfall over South China from May to August. Adv. Atmos. Sci., 31, 273282, https://doi.org/10.1007/s00376-013-3051-x.

    • Search Google Scholar
    • Export Citation
  • Sun, T., S. Yao, and Q. Huang, 2022: The atmospheric quasi-biweekly oscillation during the Jiangnan Meiyu onset period. Front. Earth Sci., 10, 986830, https://doi.org/10.3389/feart.2022.986830.

    • Search Google Scholar
    • Export Citation
  • Tao, S. Y., and L. X. Chen, 1987: A review of recent research on the East Asian summer monsoon in China. Monsoon Meteorology, C. P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 60–92.

  • Vitart, F., and Coauthors, 2017: The Subseasonal to Seasonal (S2S) prediction project database. Bull. Amer. Meteor. Soc., 98, 163173, https://doi.org/10.1175/BAMS-D-16-0017.1.

    • Search Google Scholar
    • Export Citation
  • Wang, B., and X. Xu, 1997: Northern Hemisphere summer monsoon singularities and climatological intraseasonal oscillation. J. Climate, 10, 10711085, https://doi.org/10.1175/1520-0442(1997)010<1071:NHSMSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, B., and LinHo, 2002: Rainy season of the Asian–Pacific summer monsoon. J. Climate, 15, 386398, https://doi.org/10.1175/1520-0442(2002)015<0386:RSOTAP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, H., F. Liu, B. Wang, and T. Li, 2018: Effects of intraseasonal oscillation on South China Sea summer monsoon onset. Climate Dyn., 51, 25432558, https://doi.org/10.1007/s00382-017-4027-9.

    • Search Google Scholar
    • Export Citation
  • Wang, J., J. He, X. Liu, and B. Wu, 2009: Interannual variability of the Meiyu onset over Yangtze-Huaihe River Valley and analyses of its previous strong influence signal. Chin. Sci. Bull., 54, 687695, https://doi.org/10.1007/s11434-008-0534-8.

    • Search Google Scholar
    • Export Citation
  • Wei, K., H. Duan, Y. Li, M. Chen, J. Ma, H. An, and S. Zhou, 2020: Reflections on the catastrophic 2020 Yangtze River Basin flooding in southern China. Innovation, 1, 100038, https://doi.org/10.1016/j.xinn.2020.100038.

    • Search Google Scholar
    • Export Citation
  • Wu, J., and X. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets (in Chinese). Chin. J. Geophys., 56, 11021111, https://doi.org/10.6038/cjg20130406.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., Q. Huang, S. Yao, and T. Sun, 2021: Multiscale causes of persistent heavy rainfall in the Meiyu period over the middle and lower reaches of the Yangtze River. Front. Earth Sci., 9, 700878, https://doi.org/10.3389/feart.2021.700878.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., S. Yao, T. Sun, and Z. Guo, 2023: Role of the low-latitude quasi-biweekly oscillation in the extreme persistent heavy rainfall in the Mei-Yu season over the middle and lower reaches of the Yangtze River. J. Climate, 36, 38173832, https://doi.org/10.1175/JCLI-D-22-0343.1.

    • Search Google Scholar
    • Export Citation
  • Xie, J., J. Yu, H. Chen, and P.-C. Hsu, 2020: Sources of subseasonal prediction skill for heatwaves over the Yangtze River Basin revealed from three S2S models. Adv. Atmos. Sci., 37, 14351450, https://doi.org/10.1007/s00376-020-0144-1.

    • Search Google Scholar
    • Export Citation
  • Xie, J., P.-C. Hsu, P. Ray, K. Li, and W. Yu, 2022: Mechanism of MJO-modulated triggering on the rainy season onset over Indian subcontinent. Mon. Wea. Rev., 150, 19371951, https://doi.org/10.1175/MWR-D-21-0275.1.

    • Search Google Scholar
    • Export Citation
  • Xie, J., P.-C. Hsu, Y. Hu, M. Ye, and J. Yu, 2023: Skillful extended-range forecast of rainfall and extreme events in East China based on deep learning. Wea. Forecasting, 38, 467486, https://doi.org/10.1175/WAF-D-22-0132.1.

    • Search Google Scholar
    • Export Citation
  • Xu, Y., X. Gao, Y. Shen, C. Xu, Y. Shi, and F. Giorgi, 2009: A daily temperature dataset over China and its application in validating a RCM simulation. Adv. Atmos. Sci., 26, 763772, https://doi.org/10.1007/s00376-009-9029-z.

    • Search Google Scholar
    • Export Citation
  • Yanai, M., S. Esbensen, and J.-H. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large scale heat and moisture budgets. J. Atmos. Sci., 30, 611627, https://doi.org/10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yang, J., T. Zhu, M. Gao, H. Lin, B. Wang, and Q. Bao, 2018: Late-July barrier for subseasonal forecast of summer daily maximum temperature over Yangtze River Basin. Geophys. Res. Lett., 45, 12 61012 615, https://doi.org/10.1029/2018GL080963.

    • Search Google Scholar
    • Export Citation
  • Yao, Y., H. Lin, and Q. Wu, 2019: Linkage between interannual variation of the East Asian intraseasonal oscillation and mei-yu onset. J. Climate, 32, 145160, https://doi.org/10.1175/JCLI-D-17-0873.1.

    • Search Google Scholar
    • Export Citation
  • Ye, D., and R. Huang, 1996: Research on the Regularity and Cause of Droughts and Floodings in the Yangtze River Valley and the Yellow River Valley (in Chinese). Shandong Science and Technology Press, 387 pp.

  • Ye, H., and R. Lu, 2011: Subseasonal variation in ENSO-related East Asian rainfall anomalies during summer and its role in weakening the relationship between the ENSO and summer rainfall in eastern China since the late 1970s. J. Climate, 24, 22712284, https://doi.org/10.1175/2010JCLI3747.1.

    • Search Google Scholar
    • Export Citation
  • Ye, Q., and M. H. Glantz, 2005: The 1998 Yangtze floods: The use of short-term forecasts in the context of seasonal to interannual water resource management. Mitigation Adapt. Strategies Global Change, 10, 159182, https://doi.org/10.1007/s11027-005-7838-7.

    • Search Google Scholar
    • Export Citation
  • Ye, T.-S., R. Zhi, J.-H. Zhao, and Z.-Q. Gong, 2014: The two annual northward jumps of the West Pacific subtropical high and their relationship with summer rainfall in eastern China under global warming. Chin. Phys., 23B, 069203, https://doi.org/10.1088/1674-1056/23/6/069203.

    • Search Google Scholar
    • Export Citation
  • Ye, Y., and C. Qian, 2021: Conditional attribution of climate change and atmospheric circulation contributing to the record-breaking precipitation and temperature event of summer 2020 in southern China. Environ. Res. Lett., 16, 044058, https://doi.org/10.1088/1748-9326/abeeaf.

    • Search Google Scholar
    • Export Citation
  • Zhang, K., J. Li, Z. Zhu, and T. Li, 2021: Implications from subseasonal prediction skills of the prolonged heavy snow event over southern China in early 2008. Adv. Atmos. Sci., 38, 18731888, https://doi.org/10.1007/s00376-021-0402-x.

    • Search Google Scholar
    • Export Citation
  • Zhou, C., L. Du, W. Gao, and G. Guo, 2020: Application of CFSv2 in extended-range forecast of Meiyu characteristics in Hubei Province (in Chinese). Torrential Rain Disaster, 39, 185191, https://doi.org/10.3969/j.issn.1004-9045.2020.02.009.

    • Search Google Scholar
    • Export Citation
  • Zhou, P., Y. Zhou, J. Jin, S. Ning, Y. Cui, and C. Wu, 2020: Meiyu identification of Yangtze-Huaihe region in Anhui province based on Meiyu monitoring indices (in Chinese). Hydrol. Sci. Eng., 6, 915, https://doi.org/10.12170/20200823003.

    • Search Google Scholar
    • Export Citation
  • Zhou, Y., Y. Zuo, Y. Zhang, J. Jin, P. Zhou, C. Wu, Y. Cui, and S. Ning, 2021: Identification and characteristics analysis of Meiyu in Anhui province based on the national standard of Meiyu monitoring indices. Hydrol. Res., 52, 975989, https://doi.org/10.2166/nh.2021.042.

    • Search Google Scholar
    • Export Citation
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