Abstract
Skillful subseasonal prediction is crucial for meteorological disaster prevention and risk management. In this study, the subseasonal prediction skills of the new-generation coupled model of Beijing Climate Center (named as BCC-CSM2-HR) were evaluated, and a dynamical-statistical prediction model (DSPM) was developed to further improve pentad-mean precipitation predictions in China. The results show that although BCC-CSM2-HR can generally capture the climatological rain belt movement over eastern China, its skillful predictions for rainfall anomalies are basically confined within 3 pentads. By combining the dynamical model output and statistical method, a DSPM was built to capture the simultaneously coupled evolving patterns between anomalous precipitation and its atmospheric circulation predictors for each subregion of China, which was divided in terms of a cluster analysis. The 9-year independent validation shows that the prediction skills of DSPM had been significantly improved after 3 forecast pentads compared with the original model forecast. The skillful prediction can persist for a 6-pentad lead especially over the northern China and the Yangtze-Huaihe River Basin in the DSPM. As the major predictability sources of subseasonal forecasts, the Madden–Julian oscillation (MJO) and boreal summer intraseasonal oscillation (BSISO) are skillfully predicted by the BCC model for up to 23 days and 10–13 days, respectively. As a result, the improved performance of the DSPM can be largely attributed to its more realistic representation of MJO and BSISO associated circulation anomalies.
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Data availability
The atmospheric circulation and moisture data are from NCEP/NCAR Reanalysis data (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.pressure.html). The precipitation data are from CRA-40/Land dataset (http://www.nmic.cn/data/cdcdetail/dataCode/NAFP_CRA40_FTM_3HOR_NC.html). The NOAA OLR data are available at (https://www.esrl.noaa.gov/psd/data/gridded/data.interp_OLR.htmlhttps://www.esrl.noaa.gov/psd/data/gridded/data.interp_OLR.html). The hindcast data of BCC-CSM2-HR can be downloaded from (http://s2s.cma.cn/centers?mo=babj_CMA_37). Enquiries about post-processed data availability should be directed to the authors.
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Acknowledgements
The computations were supported by the Advanced Computing East China Sub-center and the China Meterological Administration (CMA) Shuguang-PI high-performance computing platform and uses of the computational resources is gratefully acknowledged.
Funding
This study was jointly sponsored by National Natural Science Foundation of China (41905067 and 42175052), the Basic Research and Operational Special Project of CAMS (2021Z007), the National Key Research and Development Program (2021YFA0718000), the Innovative Development Special Project of China Meteorological Administration (CXFZ2021Z011 and CXFZ2021Z010), and the China Meteorological Administration Forecaster Program (CMAYBY2020-166).
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Wu, J., Ren, HL., Zhang, P. et al. The dynamical-statistical subseasonal prediction of precipitation over China based on the BCC new-generation coupled model. Clim Dyn 59, 1213–1232 (2022). https://doi.org/10.1007/s00382-022-06187-3
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DOI: https://doi.org/10.1007/s00382-022-06187-3