Angus, M., and G. C. Leckebusch, 2020: On the dependency of Atlantic hurricane and European windstorm hazards. Geophys. Res. Lett., 47, e2020GL090446, https://doi.org/10.1029/2020GL090446.
Befort, D. J., K. I. Hodges, and G. C. Leckebusch, 2016: East Asian rainfall in CMIP5 models: Contribution of tropical cyclones and Mei-yu front to spatio-temporal rainfall variability. Preprints. AGU Fall Meeting 2016, San Francisco.
Befort, D. J., K. Hodges, and G. C. Leckebusch, 2017: A new approach for estimating projected future changes in extreme rainfall over East Asia and its uncertainties including information about model performance on different scales. Preprints, AGU Fall Meeting 2017, New Orleans
Bell, B., and Coauthors, 2020a: ERA5 hourly data on pressure levels from 1950 to 1978 (preliminary version). Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Accessed: 15 June 2021. [Available online from https://cds.climate.copernicus-climate.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-preliminary-back-extension?tab=overview]
Bell, B., and Coauthors, 2020b: ERA5 hourly data on single levels from 1950 to 1978 (preliminary version). Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Accessed: 15 June 2021. [Available online from https://cds.climate.copernicus-climate.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-preliminary-back-extension?tab=overview]
Bello, G. A., M. Angus, N. Pedemane, J. K. Harlalka, F. H. M. Semazzi, V. Kumar, and N. F. Samatova, 2015: Response-guided community detection: Application to climate index discovery. Proc. Joint European Conf. on Machine Learning and Knowledge Discovery in Databases, Porto, Springer, 736−751,
Bengtsson, L., S. Hagemann, and K. I. Hodges, 2004: Can climate trends be calculated from reanalysis data? J. Geophys. Res., 109, D11111, https://doi.org/10.1029/2004JD004536.
Black, E., J. Slingo, and K. R. Sperber, 2003: An observational study of the relationship between excessively strong short rains in coastal East Africa and Indian Ocean SST. Mon. Wea. Rev., 131, 74−94, https://doi.org/10.1175/1520-0493(2003)131<0074:AOSOTR>2.0.CO;2.
Chaudhary, M. S., and Coauthors, 2016: Causality-guided feature selection. Proc. 12th International Conf. on Advanced Data Mining and Applications, Gold Coast, Springer, 391−405,
Chen, J. P., Z. P. Wen, R. G. Wu, Z. S. Chen, and P. Zhao, 2015: Influences of northward propagating 25–90-day and quasi-biweekly oscillations on eastern China summer rainfall. Climate Dyn., 45, 105−124, https://doi.org/10.1007/s00382-014-2334-y.
Choi, K.-S., C.-C. Wu, and E.-J. Cha, 2010: Change of tropical cyclone activity by Pacific-Japan teleconnection pattern in the western North Pacific. J. Geophys. Res., 115, D19114, https://doi.org/10.1029/2010JD013866.
Di Capua, G., and Coauthors, 2019: Long-lead statistical forecasts of the Indian summer monsoon rainfall based on causal precursors. Wea. Forecasting, 34, 1377−1394, https://doi.org/10.1175/WAF-D-19-0002.1.
Ding, Y. H., and J. C. L. Chan, 2005: The East Asian summer monsoon: An overview. Meteorol. Atmos. Phys., 89, 117−142, https://doi.org/10.1007/s00703-005-0125-z.
Ding, Y. H., and Y. Y. Liu, 2008: A study of the teleconnection in the Asian-Pacific monsoon region. Acta Meteorologica Sinica, 66, 670−682, https://doi.org/10.11676/qxxb2008.062. (in Chinese with English abstract
Ding, Y. H., Z. Y. Wang, and Y. Sun, 2008: Inter-decadal variation of the summer precipitation in East China and its association with decreasing Asian summer monsoon. Part I: Observed evidences. International Journal of Climatology, 28, 1139−1161, https://doi.org/10.1002/joc.1615.
Ding, Y. H., P. Liang, Y. J. Liu, and Y. C. Zhang, 2020: Multiscale variability of Meiyu and its prediction: A new review. J. Geophys. Res., 125, e2019JD031496, https://doi.org/10.1029/2019JD031496.
Ding, Y. H., Y. Y. Liu, and Z.-Z. Hu, 2021: The record-breaking Mei-yu in 2020 and associated atmospheric circulation and tropical SST anomalies. Adv. Atmos. Sci., 38, 1980−1993, https://doi.org/10.1007/s00376-021-0361-2.
Ebert-Uphoff, I., and Y. Deng, 2012: A new type of climate network based on probabilistic graphical models: Results of boreal winter versus summer. Geophys. Res. Lett., 39, L19701, https://doi.org/10.1029/2012GL053269.
ECMWF, 2021: ERA5 back extension 1950−1978 (Preliminary version): Tropical cyclones are too intense. [Available online from https://confluence.ecmwf.int/display/CKB/ERA5+back+extension+1950-1978+%28Preliminary+version%29%3A+tropical+cyclones+are+too+intense]
Flato, G., and Coauthors, 2013: Evaluation of Climate Models. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker et al., Eds., Cambridge University Press, 1535 pp.
Gan, N., 2020: China has just contained the coronavirus. Now it's battling some of the worst floods in decades. CNN. [Available from https://edition.cnn.com/2020/07/14/asia/china-flood-coronavirus-intl-hnk/index.html (Access date: 25 June 2021)]
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999−2049, https://doi.org/10.1002/qj.3803.
Hodges, K., A. Cobb, and P. L. Vidale, 2017: How well are tropical cyclones represented in reanalysis datasets? J. Climate, 30, 5243−5264, https://doi.org/10.1175/JCLI-D-16-0557.1.
Jiao, D. L., N. N. Xu, F. Yang, and K. Xu, 2021: Evaluation of spatial-temporal variation performance of ERA5 precipitation data in China. Scientific Reports, 11, 17956, https://doi.org/10.1038/s41598-021-97432-y.
Kim, J.-S., R. C.-Y. Li, and W. Zhou, 2012: Effects of the Pacific-Japan teleconnection pattern on tropical cyclone activity and extreme precipitation events over the Korean peninsula. J. Geophys. Res., 117, D18109, https://doi.org/10.1029/2012JD017677.
Kosaka, Y., S.-P. Xie, and H. Nakamura, 2011: Dynamics of interannual variability in summer precipitation over East Asia. J. Climate, 24, 5435−5453, https://doi.org/10.1175/2011JCLI4099.1.
Kretschmer, M., D. Coumou, J. F. Donges, and J. Runge, 2016: Using causal effect networks to analyze different arctic drivers of midlatitude winter circulation. J. Climate, 29, 4069−4081, https://doi.org/10.1175/JCLI-D-15-0654.1.
Kretschmer, M., J. Runge, and D. Coumou, 2017: Early prediction of extreme stratospheric polar vortex states based on causal precursors. Geophys. Res. Lett., 44, 8592−8600, https://doi.org/10.1002/2017GL074696.
Lavers, D. A., S. Harrigan, and C. Prudhomme, 2021: Precipitation biases in the ECMWF integrated forecasting system. Journal of Hydrometeorology, 22, 1187−1198, https://doi.org/10.1175/JHM-D-20-0308.1.
Leckebusch, G. C., D. J. Befort, and K. I. Hodges, 2016: Extreme Events in China under Climate Change: Uncertainty and related impacts (CSSP-FOREX). Preprints, EGU General Assembly 2016, Vienna, EGU.
Li, R. C. Y., W. Zhou, and T. Li, 2014: Influences of the pacific–Japan teleconnection pattern on synoptic-scale variability in the Western North Pacific. J. Climate, 27, 140−154, https://doi.org/10.1175/JCLI-D-13-00183.1.
Li, R. K. K., C. Y. Tam, N. C. Lau, S. J. Sohn, and J. B. Ahn, 2020: Potential predictability of the silk road pattern and the role of SST as inferred from seasonal hindcast experiments of a coupled climate Model. J. Climate, 33, 9567−9580, https://doi.org/10.1175/JCLI-D-20-0235.1.
Li, W. J., H.-C. Ren, J. Q. Zuo, and H.-L. Ren, 2018: Early summer southern China rainfall variability and its oceanic drivers. Climate Dyn., 50, 4691−4705, https://doi.org/10.1007/s00382-017-3898-0.
Liu, B. Q., Y. H. Yan, C. W. Zhu, S. M. Ma, and J. Y. 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.
Liu, Y. Y., and Y. H. Ding, 2008: Teleconnection between the Indian summer monsoon onset and the Meiyu over the Yangtze River Valley. Science in China Series D: Earth Sciences, 51, 1021−1035, https://doi.org/10.1007/s11430-008-0073-9.
Lu, R. Y., 2002: Indices of the summertime western North Pacific subtropical high. Adv. Atmos. Sci., 19, 1004−1028, https://doi.org/10.1007/s00376-002-0061-5.
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, 29−41, https://doi.org/10.1007/s00376-019-9051-8.
Ng, K. S., and G. C. Leckebusch, 2021: A new view on the risk of typhoon occurrence in the western North Pacific. Natural Hazards and Earth System Sciences, 21, 663−682, https://doi.org/10.5194/nhess-21-663-2021.
Ning, L., J. Liu, and B. Wang, 2017: How does the South Asian High influence extreme precipitation over eastern China? J. Geophys. Res., 122, 4281−4298, https://doi.org/10.1002/2016JD026075.
Ninomiya, K., and Y. Shibagaki, 2007: Multi-scale features of the Meiyu-Baiu front and associated precipitation systems. J. Meteor. Soc. Japan, 85B, 103−122, https://doi.org/10.2151/jmsj.85B.103.
Nitta, T., 1987: Convective activities in the tropical Western Pacific and their impact on the northern hemisphere summer Circulation. J. Meteor. Soc. Japan, 65, 373−390, https://doi.org/10.2151/jmsj1965.65.3_373.
Osinski, R., and Coauthors, 2016: An approach to build an event set of European windstorms based on ECMWF EPS. Natural Hazards and Earth System Sciences, 16, 255−268, https://doi.org/10.5194/nhess-16-255-2016.
Runge, J., 2015: Quantifying information transfer and mediation along causal pathways in complex systems. Physical Review E, 92, 062829, https://doi.org/10.1103/PhysRevE.92.062829.
Runge, J., J. Heitzig, N. Marwan, and J. Kurths, 2012a: Quantifying causal coupling strength: A lag-specific measure for multivariate time series related to transfer entropy. Physical Review E, 86, 061121, https://doi.org/10.1103/PhysRevE.86.061121.
Runge, J., J. Heitzig, V. Petoukhov, and J. Kurths, 2012b: Escaping the curse of dimensionality in estimating multivariate transfer entropy. Physical Review Letters, 108, 258701, https://doi.org/10.1103/PhysRevLett.108.258701.
Runge, J., V. Petoukhov, and J. Kurths, 2014: Quantifying the strength and delay of climatic interactions: The ambiguities of cross correlation and a novel measure based on graphical models. J. Climate, 27, 720−739, https://doi.org/10.1175/JCLI-D-13-00159.1.
Runge, J., and Coauthors, 2015: Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502, https://doi.org/10.1038/ncomms9502.
Runge, J., and Coauthors, 2019a: Inferring causation from time series in Earth system sciences. Nature Communications, 10, 2553, https://doi.org/10.1038/s41467-019-10105-3.
Runge, J., P. Nowack, M. Kretschmer, S. Flaxman, and D. Sejdinovic, 2019b: Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances, 5, eaau4996, https://doi.org/10.1126/sciadv.aau4996.
Runge, J., 2020: Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), P. Jonas, and S. David, Eds., PMLR, 1388−1397.
Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole mode in the tropical Indian Ocean. Nature, 401, 360−363, https://doi.org/10.1038/43854.
Sall, C., 2013: Climate trends and impacts in China. World Bank Group, Washington D.C. [Available online from https://openknowledge.worldbank.org/handle/10986/17558]
Sampe, T., and S.-P. Xie, 2010: Large-scale dynamics of the Meiyu-Baiu rainband: Environmental forcing by the westerly jet. J. Climate, 23, 113−134, https://doi.org/10.1175/2009JCLI3128.1.
Spirtes, P., C. Glymour, and R. Scheines, 2001: Causation, Prediction, and Search. MIT Press.
Thompson, V., N. J. Dunstone, A. A. Scaife, D. M. Smith, J. M. Slingo, S. Brown, and S. E. Belcher, 2017: High risk of unprecedented UK rainfall in the current climate. Nature Communications, 8, 107, https://doi.org/10.1038/s41467-017-00275-3.
Tomita, T., T. Yamaura, and T. Hashimoto, 2011: Interannual variability of the Baiu season near Japan evaluated from the equivalent potential temperature. J. Meteor. Soc. Japan, 89, 517−537, https://doi.org/10.2151/jmsj.2011-507.
Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78, 2771−2778, https://doi.org/10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.
Wakabayashi, S., and R. Kawamura, 2004: Extraction of major teleconnection patterns possibly associated with the anomalous summer climate in Japan. J. Meteor. Soc. Japan, 82, 1577−1588, https://doi.org/10.2151/jmsj.82.1577.
Walz, M. A., and G. C. Leckebusch, 2019: Loss potentials based on an ensemble forecast: How likely are winter windstorm losses similar to 1990? Atmospheric Science Letters, 20, e891, https://doi.org/10.1002/asl.891.
Wang, B., and Z. Fan, 1999: Choice of South Asian summer monsoon indices. Bull. Amer. Meteor. Soc., 80, 629−638, https://doi.org/10.1175/1520-0477(1999)080<0629:COSASM>2.0.CO;2.
Wang, B., and LinHo, 2002: Rainy season of the Asian–pacific summer monsoon. J. Climate, 15, 386−398, https://doi.org/10.1175/1520-0442(2002)015<0386:RSOTAP>2.0.CO;2.
Wang, B., R. G. Wu, and K.-M. Lau, 2001: Interannual variability of the Asian summer monsoon: Contrasts between the Indian and the Western North Pacific–East Asian monsoons. J. Climate, 14, 4073−4090, https://doi.org/10.1175/1520-0442(2001)014<4073:IVOTAS>2.0.CO;2.
Wang, B., Z. W. Wu, J. P. Li, J. Liu, C.-P. Chang, Y. H. Ding, and G. X. Wu, 2008: How to measure the strength of the East Asian summer monsoon. J. Climate, 21, 4449−4463, https://doi.org/10.1175/2008JCLI2183.1.
Wang, B., J. Liu, J. Yang, T. J. Zhou, and Z. W. Wu, 2009: Distinct principal modes of early and late summer rainfall anomalies in East Asia. J. Climate, 22, 3864−3875, https://doi.org/10.1175/2009JCLI2850.1.
Wang, H. J., and S. P. He, 2015: The North China/Northeastern Asia severe summer drought in 2014,. J. Climate, 28, 6667−6681, https://doi.org/10.1175/JCLI-D-15-0202.1.
Webster, P. J., and S. Yang, 1992: Monsoon and Enso: Selectively interactive systems. Quart. J. Roy. Meteor. Soc., 118, 877−926, https://doi.org/10.1002/qj.49711850705.
Wu, J., and X.-J. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets. Chinese Journal of Geophysics, 56, 1102−1111, https://doi.org/10.6038/cjg20130406. (in Chinese with English abstract
Wu, R. G., Z.-Z. Hu, and B. P. Kirtman, 2003: Evolution of ENSO-related rainfall anomalies in East Asia. J. Climate, 16, 3742−3758, https://doi.org/10.1175/1520-0442(2003)016<3742:EOERAI>2.0.CO;2.
Xu, Y., X. J. Gao, Y. Shen, C. H. 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, 763−772, https://doi.org/10.1007/s00376-009-9029-z.
Xue, X., W. Chen, D. Nath, and D. W. Zhou, 2015: Whether the decadal shift of South Asia High intensity around the late 1970s exists or not. Theor. Appl. Climatol., 120, 673−683, https://doi.org/10.1007/s00704-014-1200-5.
Ye, H., and R. Y. 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, 2271−2284, https://doi.org/10.1175/2010JCLI3747.1.
Zhou, B. T., and H. J. Wang, 2006: Relationship between the boreal spring Hadley circulation and the summer precipitation in the Yangtze River valley. J. Geophys. Res., 111, D16109, https://doi.org/10.1029/2005JD007006.