ECMWF: S2S, ECMWF, Realtime, Daily averaged,
http://apps.ecmwf.int/datasets/data/s2s/ (last access: 20 November 2023), 2023.
Eklund, J. and Karlsson, S.: Forecast Combination and Model Averaging Using Predictive Measures, Econ. Rev., 26, 329–363, https://doi.org/10.1080/07474930701220550, 2007.
GloH20: MSWEP – Multi-Source Weighted-Ensemble Precipitation,
http://www.gloh2o.org/mswep/ (last access: 20 November 2023), 2023.
Guo, B., Xu, T., Yang, Q., Zhang, J., Dai, Z., Deng, Y., and Zou, J.: Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China, Remote Sens., 15, 1373, https://doi.org/10.3390/rs15051373, 2023.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz©-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, 2020.
Hsu, P.-C., Li, T., You, L., Gao, J., and Ren, H.-L.: A spatial–temporal projection model for 10–30 day rainfall forecast in South China, Clim. Dynam., 44, 1227–1244, https://doi.org/10.1007/s00382-014-2215-4, 2015.
Hsu, P. C., Lee, J. Y., and Ha, K. J.: Influence of boreal summer intraseasonal oscillation on rainfall extremes in southern China, Int. J. Climatol., 36, 1403–1412, https://doi.org/10.1002/joc.4433, 2016.
Jie, W. H., Vitart, F., Wu, T. W., and Liu, X. W.: Simulations of the Asian summer monsoon in the sub-seasonal to seasonal prediction project (S2S) database, Q. J. Roy. Meteorol. Soc., 143, 2282–2295, https://doi.org/10.1002/qj.3085, 2017.
Lee, S.-S., Wang, B., Waliser, D. E., Neena, J. M., and Lee, J.-Y.: Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment, Clim. Dynam., 45, 2123–2135, https://doi.org/10.1007/s00382-014-2461-5, 2015.
Li, W., Yang, X.-Q., Fang, J., Tao, L., and Sun, X.: Asymmetric boreal summer intraseasonal oscillation events over the western North Pacific and their impacts on East Asian precipitation, J. Climate, 36, 2645–2661, 2023.
Li, Y., Wu, Z., He, H., Wang, Q. J., Xu, H., and Lu, G.: Post-processing sub-seasonal precipitation forecasts at various spatiotemporal scales across China during boreal summer monsoon, J. Hydrol., 598, 125742, https://doi.org/10.1016/j.jhydrol.2020.125742, 2021.
Li, Y., Wu, Z., He, H., and Yin, H.: Probabilistic subseasonal precipitation forecasts using preceding atmospheric intraseasonal signals in a Bayesian perspective, Hydrol. Earth Syst. Sci., 26, 4975–4994, https://doi.org/10.5194/hess-26-4975-2022, 2022.
Li, Y., Pang, B., Zheng, Z., Chen, H., Peng, D., Zhu, Z., and Zuo, D.: Evaluation of Four Satellite Precipitation Products over Mainland China Using Spatial Correlation Analysis, Remote Sens., 15, 1823, https://doi.org/10.3390/rs15071823, 2023.
Liu, B., Zhu, C., Ma, S., Yan, Y., and Jiang, N.: Subseasonal processes of triple extreme heatwaves over the Yangtze River Valley in 2022, Weather Extrem., 40, 100572, https://doi.org/10.1016/j.wace.2023.100572, 2023.
Liu, F., Ouyang, Y., Wang, B., Yang, J., Ling, J., and Hsu, P.-C.: Seasonal evolution of the intraseasonal variability of China summer precipitation, Clim. Dynam., 54, 4641–4655, 2020.
Liu, J. and Lu, R.: Different Impacts of Intraseasonal Oscillations on Precipitation in Southeast China between Early and Late Summers, Adv. Atmos. Sci., 39, 1885–1896, 2022.
Liu, J., Shangguan, D., Liu, S., Ding, Y., Wang, S., and Wang, X.: Evaluation and comparison of CHIRPS and MSWEP daily-precipitation products in the Qinghai-Tibet Plateau during the period of 1981–2015, Atmos. Res., 230, 104634, https://doi.org/10.1016/j.atmosres.2019.104634, 2019.
Livezey, R. E. and Chen, W. Y.: Statistical Field Significance and its Determination by Monte Carlo Techniques, Mon. Weather Rev., 111, 46–59, https://doi.org/10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2, 1983.
NOAA: NOAA's Outgoing Longwave Radiation – Daily Climate Data Record (OLR – Daily CDR): PSL Interpolated Version data provided by the NOAA PSL, Boulder, Colorado [data set],
https://psl.noaa.gov/thredds/catalog/Datasets/olrcdr/catalog.html (last access: 20 November 2023), 2023.
Peng, Z., Wang, Q., Bennett, J. C., Schepen, A., Pappenberger, F., Pokhrel, P., and Wang, Z.: Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China, J. Geophys. Res.-Atmos., 119, 7116–7135, 2014.
Ren, P., Ren, H. L., Fu, J. X., Wu, J., and Du, L.: Impact of boreal summer intraseasonal oscillation on rainfall extremes in southeastern China and its predictability in CFSv2, J. Geophys. Res.-Atmos., 123, 4423–4442, 2018.
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.: Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors, Water Resour. Res., 46, W05521, https://doi.org/10.1029/2009WR008328, 2010.
Schepen, A., Wang, Q. J., and Robertson, D. E.: Seasonal Forecasts of Australian Rainfall through Calibration and Bridging of Coupled GCM Outputs, Mon. Weather Rev., 142, 1758–1770, https://doi.org/10.1175/MWR-D-13-00248.1, 2014.
Schepen, A., Wang, Q., and Everingham, Y.: Calibration, bridging, and merging to improve GCM seasonal temperature forecasts in Australia, Mon. Weather Rev., 144, 2421–2441, 2016.
Shibuya, R., Nakano, M., Kodama, C., Nasuno, T., Kikuchi, K., Satoh, M., Miura, H., and Miyakawa, T.: Prediction Skill of the Boreal Summer Intra-Seasonal Oscillation in Global Non-hydrostatic Atmospheric Model Simulations with Explicit Cloud Microphysics, J. Meteorol. Soc. Jpn. Ser. II, 99, 973–992, https://doi.org/10.2151/jmsj.2021-046, 2021.
Slater, L. J., Arnal, L., Boucher, M. A., Chang, A. Y. Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., Villarini, G., Wilby, R. L., Wood, A., and Zappa, M.: Hybrid forecasting: blending climate predictions with AI models, Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, 2023.
Specq, D. and Batté, L.: Improving subseasonal precipitation forecasts through a statistical–dynamical approach: application to the southwest tropical Pacific, Clim. Dynam., 55, 1913–1927, https://doi.org/10.1007/s00382-020-05355-7, 2020.
Stock, J. H. and Watson, M. W.: Chapter 10 Forecasting with Many Predictors, in: Handbook of Economic Forecasting, edited by: Elliott, G., Granger, C. W. J., and Timmermann, A., Elsevier, 515–554, https://doi.org/10.1016/S1574-0706(05)01010-4, 2006.
Strazzo, S., Collins, D. C., Schepen, A., Wang, Q., Becker, E., and Jia, L.: Application of a hybrid statistical–dynamical system to seasonal prediction of North American temperature and precipitation, Mon. Weather Rev., 147, 607–625, 2019a.
Strazzo, S., Collins, D. C., Schepen, A., Wang, Q. J., Becker, E., and Jia, L.: Application of a Hybrid Statistical-Dynamical System to Seasonal Prediction of North American Temperature and Precipitation, Mon. Weather Rev., 147, 607–625, https://doi.org/10.1175/MWR-D-18-0156.1, 2019b.
Vitart, F. and Robertson, A. W.: The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events, npj Clim. Atmos. Sci., 1, 1–7, 2018.
Wang, Q., Shao, Y., Song, Y., Schepen, A., Robertson, D. E., Ryu, D., and Pappenberger, F.: An evaluation of ECMWF SEAS5 seasonal climate forecasts for Australia using a new forecast calibration algorithm, Environ. Model. Softw., 122, 104550, https://doi.org/10.1016/j.envsoft.2019.104550, 2019.
White, C. J., Domeisen, D. I., Acharya, N., Adefisan, E. A., Anderson, M. L., Aura, S., Balogun, A. A., Bertram, D., Bluhm, S., and Brayshaw, D. J.: Advances in the application and utility of subseasonal-to-seasonal predictions, B. Am. Meteorol. Soc., 103, E1448–E1472, 2022.
Wu, J., Ren, H.-L., Zhang, P., Wang, Y., Liu, Y., Zhao, C., and Li, Q.: The dynamical-statistical subseasonal prediction of precipitation over China based on the BCC new-generation coupled model, Clim. Dynam., 59, 1213–1232, 2022.
Wu, J., Li, J., Zhu, Z., and Hsu, P.-C.: Factors determining the subseasonal prediction skill of summer extreme rainfall over southern China, Clim. Dynam., 60, 443–460, https://doi.org/10.1007/s00382-022-06326-w, 2023.
Xie, J., Hsu, P.-C., Hu, Y., Ye, M., and Yu, J.: Skillful Extended-Range Forecast of Rainfall and Extreme Events in East China Based on Deep Learning, Weather Forecast., 38, 467–486, https://doi.org/10.1175/WAF-D-22-0132.1, 2023.
Yan, Y., Zhu, C., and Liu, B.: Subseasonal predictability of the July 2021 extreme rainfall event over Henan China in S2S operational models, J. Geophys. Res.-Atmos., 128, e2022JD037879, https://doi.org/10.1029/2022JD037879, 2023.
Zhang, K., Li, J., Zhu, Z., and Li, T.: Implications from Subseasonal Prediction Skills of the Prolonged Heavy Snow Event over Southern China in Early 2008, Adv. Atmos. Sci., 38, 1873–1888, https://doi.org/10.1007/s00376-021-0402-x, 2021.
Zhu, C., Liu, B., Li, L., Ma, S., Jiang, N., and Yan, Y.: Progress and Prospects of Research on Subseasonal to Seasonal Variability and Prediction of the East Asian Monsoon, J. Meteorol. Res., 36, 677–690, 2022.
Zhu, Z. and Li, T.: The statistical extended-range (10–30-day) forecast of summer rainfall anomalies over the entire China, Clim. Dynam., 48, 209–224, https://doi.org/10.1007/s00382-016-3070-2, 2017.
Zhu, Z., Li, T., Hsu, P.-C., and He, J.: A spatial-temporal projection model for extended-range forecast in the tropics, Clim. Dynam., 45, 1085–1098, https://doi.org/10.1007/s00382-014-2353-8, 2015.
Zhu, Z., Zhou, Y., Jiang, W., Fu, S., and Hsu, P.: Influence of compound zonal displacements of the South Asia high and the western Pacific subtropical high on Meiyu intraseasonal variation, Clim. Dynam., 61, 3309–3325, https://doi.org/10.1007/s00382-023-06726-6, 2023.
