[1] Bett, P. E., A. A. Scaife, C. F. Li, et al., 2018: Seasonal forecasts of the summer 2016 Yangtze River basin rainfall. Adv. Atmos. Sci., 35, 918–926. doi: 10.1007/s00376-018-7210-y
[2] Bett, P. E., N. Martin, A. A. Scaife, et al., 2020: Seasonal rainfall forecasts for the Yangtze River basin of China in summer 2019 from an improved climate service. J. Meteor. Res., 34, 904–916. doi: 10.1007/s13351-020-0049-z
[3] Camargo, S. J., J. Camp, R. L. Elsberry, et al., 2019: Tropical cyclone prediction on subseasonal time-scales. Trop. Cycl. Res. Rev., 8, 150–165. doi: 10.1016/j.tcrr.2019.10.004
[4] Camp, J., M. Roberts, C. MacLachlan, et al., 2015: Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system. Quart. J. Roy. Meteor. Soc., 141, 2206–2219. doi: 10.1002/qj.2516
[5] Camp, J., A. A. Scaife, and J. Heming, 2018a: Predictability of the 2017 North Atlantic hurricane season. Atmos. Sci. Lett., 19, e813. doi: 10.1002/asl.813
[6] Camp, J., M. C. Wheeler, H. H. Hendon, et al., 2018b: Skilful multiweek tropical cyclone prediction in ACCESS-S1 and the role of the MJO. Quart. J. Roy. Meteor. Soc., 144, 1337–1351. doi: 10.1002/qj.3260
[7] Camp, J., M. J. Roberts, R. E. Comer, et al., 2019: The western Pacific subtropical high and tropical cyclone landfall: Seasonal forecasts using the Met Office GloSea5 system. Quart. J. Roy. Meteor. Soc., 145, 105–116. doi: 10.1002/qj.3407
[8] Chu, J.-H., C. R. Sampson, A. S. Levine, et al., 2002: The Joint Typhoon Warning Center Tropical Cyclone Best Tracks, 1945–2000. Naval Research Laboratory Technical Report, NRL/MR/7540-02-16, 22 pp.
[9] CMA, 2019: Lekima Ranks the 5th of the Landfall Typhoons in Mainland China Since 1949. Available online at http://www.cma.gov.cn/en2014/news/News/201908/t20190820_533392.html. Accessed on 22 October 2020.
[10] CPC, 2020: Historical El Nino/La Niña episodes (1950–Present): Cold & Warm Episodes by Season. Available online at https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php. Accessed on 22 October 2020.
[11] Dee, D. P., S. M. Uppala, A. J. Simmons, et al., 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597. doi: 10.1002/qj.828
[12] Gao, S., Z. F. Chen, and W. Zhang, 2018: Impacts of tropical North Atlantic SST on western North Pacific landfalling tropical cyclones. J. Climate, 31, 853–862. doi: 10.1175/JCLI-D-17-0325.1
[13] Golding, N., C. Hewitt, P. Q. Zhang, et al., 2017: Improving user engagement and uptake of climate services in China. Climate Serv., 5, 39–45. doi: 10.1016/j.cliser.2017.03.004
[14] Golding, N., C. Hewitt, P. Q. Zhang, et al., 2019: Co-development of a seasonal rainfall forecast service: Supporting flood risk management for the Yangtze River basin. Climate Risk Manag., 23, 43–49. doi: 10.1016/j.crm.2019.01.002
[15] Gregory, P. A., J. Camp, K. Bigelow, et al., 2019: Sub-seasonal predictability of the 2017–2018 Southern Hemisphere tropi-cal cyclone season. Atmos. Sci. Lett., 20, e886. doi: 10.1002/asl.886
[16] Hewitt, C. D., N. Golding, P. Q. Zhang, et al., 2020: The process and benefits of developing prototype climate services—Examples in China. J. Meteor. Res., 34, 893–903. doi: 10.1007/s13351-020-0042-6
[17] Klotzbach, P., E. Blake, J. Camp, et al., 2019: Seasonal tropical cyclone forecasting. Trop. Cycl. Res. Rev., 8, 134–149. doi: 10.1016/j.tcrr.2019.10.003
[18] Knapp, K. R., M. C. Kruk, D. H. Levinson, et al., 2010: The international best track archive for climate stewardship (IBTrACS): Unifying tropical cyclone data. Bull. Amer. Meteor. Soc., 91, 363–376. doi: 10.1175/2009BAMS2755.1
[19] MacLachlan, C., A. Arribas, K. A. Peterson, et al., 2015: Global Seasonal forecast system version 5 (GloSea5): A high-resolution seasonal forecast system. Quart. J. Roy. Meteor. Soc., 141, 1072–1084. doi: 10.1002/qj.2396
[20] Reuters, 2019: Death Toll Rises to 44 as Typhoon Lekima Wreaks Havoc in Eastern China. Available online at https://zhuanlan.zhihu.com/p/77860355. Accessed on 22 October 2020.
[21] Tian, B. Q., and K. Fan, 2019: Seasonal climate prediction models for the number of landfalling tropical cyclones in China. J. Meteor. Res., 33, 837–850. doi: 10.1007/s13351-019-8187-x
[22] Walters, D., I. Boutle, M. Brooks, et al., 2017: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Glo-bal Land 6.0/6.1 configurations. Geosci. Model Dev., 10, 1487–1520. doi: 10.5194/gmd-10-1487-2017
[23] Wang, B., B. Q. Xiang, and J.-Y. Lee, 2013: Subtropical high predictability establishes a promising way for monsoon and tropical storm predictions. Proc. Natl. Acad. Sci. USA, 110, 2718–2722. doi: 10.1073/pnas.1214626110
[24] Wang, Y. J., S. S. Wen, X. C. Li, et al., 2016: Spatiotemporal distributions of influential tropical cyclones and associated economic losses in China in 1984–2015. Nat. Hazards, 84, 2009–2030. doi: 10.1007/s11069-016-2531-6
[25] Wang, H., M. Xu, A. Onyejuruwa, et al., 2019: Tropical cyclone damages in Mainland China over 2005–2016: Losses analy-sis and implications. Environ. Dev. Sustain., 21, 3077–3092. doi: 10.1007/s10668-019-00481-7
[26] Waters, J., D. J. Lea, M. J. Martin, et al., 2015: Implementing a variational data assimilation system in an operational 1/4 degree global ocean model. Quart. J. Roy. Meteor. Soc., 141, 333–349. doi: 10.1002/qj.2388
[27] Wen, S. S., B. D. Su, Y. J. Wang, et al., 2018: Economic sector loss from influential tropical cyclones and relationship to associated rainfall and wind speed in China. Glob. Planet. Change, 169, 224–233. doi: 10.1016/j.gloplacha.2018.08.004
[28] Wilks, D. S., 2011: Statistical forecasting. Int. Geophys., 100, 215–300. doi: 10.1016/b978-0-12-385022-5.00007-5
[29] Williams, K. D., C. M. Harris, A. Bodas-Salcedo, et al., 2015: The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci. Model Dev., 8, 1509–1524. doi: 10.5194/gmd-8-1509-2015
[30] Wood, N., A. Staniforth, A. White, et al., 2014: An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global non-hydrostatic equations. Quart. J. Roy. Meteor. Soc., 140, 1505–1520. doi: 10.1002/qj.2235
[31] Wu, M. C., W. L. Chang, and W. M. Leung, 2004: Impacts of El Niño–Southern Oscillation events on tropical cyclone landfalling activity in the western North Pacific. J. Climate, 17, 1419–1428. doi: 10.1175/1520-0442(2004)017<1419:IOENOE>2.0.CO;2
[32] Xiao, F. J., and Z. N. Xiao, 2010: Characteristics of tropical cyclones in China and their impacts analysis. Nat. Hazards, 54, 827–837. doi: 10.1007/s11069-010-9508-7
[33] Zhang, W., and G. Villarini, 2019: Seasonal forecasting of western North Pacific tropical cyclone frequency using the North American multi-model ensemble. Climate Dyn., 52, 5985–5997. doi: 10.1007/s00382-018-4490-y
[34] Zhang, W., H.-F. Graf, Y. Leung, et al., 2012: Different El niño types and tropical cyclone landfall in East Asia. J. Climate, 25, 6510–6523. doi: 10.1175/JCLI-D-11-00488.1
[35] Zhang, W., G. A. Vecchi, G. Villarini, et al., 2017: Statistical–dynamical seasonal forecast of western North Pacific and East Asia landfalling tropical cyclones using the GFDL FLOR coupled climate model. J. Climate, 30, 2209–2232. doi: 10.1175/JCLI-D-16-0487.1