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1. YX Pan & Yang J* et al., 2025 : Bridge the “ Last-mile Gap ” in Climate Services Delivery: A Dynamical-AI Hybrid Framework for Next-Month Wildfire Danger Prediction and Emergency Action, Advances in Atmospheric Sciences , DOI: 10.1007/s00376-025-5091-4
2. Yang J , Lu MQ*, et al. 2025 : The “ Last-mile Efforts ” of Subseasonal Prediction and Services for Climate Resilience and Sustainability: Review and Outlook. Adv. Atmos. Sci. , https://doi.org/10.1007/s00376-025-5256-1
3. Liu AL, Yang J* ,et al. 2025 : Assimilating summer sea ice thickness enhances predictions of Arctic sea ice and surrounding atmosphere within two months, npj Climate and atmospheric Science , https://doi.org/10.1038/s41612-025-01050-8
4. Zhu T, Lu M & Yang J* et al., 2025 : Enhancing Ready-to-Implementation subseasonal crop growth predictions in central Southwestern Asia: A machine learning-climate dynamical hybrid strategy. Agricultural and Forest Meteorology , https://doi.org/10.1016/j.agrformet.2025.11058
5. Zhu T, Zhou Y, Yang J* , Vitart F, and Bao Q, 2025 : Severe extreme cold event in Beijing-Tianjin-Hebei region tied to mid-high-latitude intraseasonal waves. Geophysical Research Letters , DOI: 10.1029/2024GL113745
6. Zhang S & Yang J* et al . , 2025 : Interannual climate anomalies modulate the subseasonal dynamical prediction skill from the regional perspective over Central Southwest Asia. Atmospheric Research , 319, 108023, https://doi.org/10.1016/j.atmosres.2025.108023
7. Zhu T & Yang J* et al . , 2024 : Boreal summer Extratropical IntraSeasonal Oscillation prediction in current S2S operational models over Eurasia. Journal of Geophysical Research: Atmospheres. DOI: 10.1029/2024JD042015
8. Fan Y & Yang J* et al . , 2024 : How does the Tibetan Plateau land thermal initial condition influence the subseasonal prediction of 2020 record-breaking Mei-yu rainfall? Journal of Geophysical Research: Atmospheres. DOI: 10.1029/2024JD041723
9. Yang J*, Zhu T*, and Frederic Vitart et al. 2024 : Synchronous Eurasian heat extremes tied to boreal summer combined extratropical intraseasonal waves. npj Climate and atmospheric Science, https://doi.org/10.1038/s41612-024-00714-1
10. Pan YX, Yang J* , et al. 2024 : How well do multi-fire danger rating indices represent China forest fire variations across multi-time scales , Environmental Research Letter , https://doi.org/10.1088/1748-9326/ad2d3d
11. Liu AL, Yang J* , et al. 2023 : Subseasonal-to-seasonal prediction of arctic sea ice Using a Fully Coupled dynamical ensemble forecast system, Atmospheric Research , https://doi.org/10.1016/j.atmosres.2023.107014
12. Yang J*, Zhu, T. and Frederic Vitart 2023 : An extratropical window of opportunity for subseasonal prediction of East Asian summer surface air temperature. npj Climate and atmospheric Science, https://doi.org/10.1038/s41612-023-00384-5
13. Zhu, T. & Yang J* et al , 2023 : Orographic mechanical and surface thermal effects of the Tibetan- Iranian Plateau on extratropical intraseasonal wave in boreal summer: numerical experiments. Environmental Research Letters, https://doi.org/10.1088/1748-9326/acd796
14. Zhu, T. & Yang J* et al , 2023 : Boreal summer extratropical intraseasonal waves over the Eurasian continent and real-time monitoring metrics. Journal of Climate, 36, 3971-3991, https://doi.org/10.1175/JCLID-22-0788.1
15. YX Pan & Yang J* et al . , 2023 : Skillful seasonal prediction of summer wildfire over Central Asia. Global and Planetary Change DOI: 10.1016/j.gloplach.2023.104043
16. YL Fan & Yang J* et al . , 2022 : Gain of one-month lead predicting spring rainfall over China: A comparison between FGOALS-f2 ensemble prediction system and its driving stretched-grid downscaling prediction system. Atmospheric Research DOI: 10.1016/j.atmosres.2022.106570
17. X Qi & Yang J*, YK Xue et al . , 2022 : Subseasonal warming of surface soil enhances precipitation over the eastern Tibetan Plateau in early summer . Journal of Geophysical Research: Atmospheres. DOI: 10.1029/2022JD037250
18. Yang, J*, SY Li, Tao Zhu, Xin Qi, JP Liu, SJ Kim, DY Gong 2022 : Intraseasonal melting of northern Barents Sea ice forced by circumpolar clockwise propagating atmospheric wave during early summer. J. Climate. DOI: 10.1175/JCLI-D-21-0538.1
19. Zhu, T & Yang, J* 2021: Two types of mid-high-latitude low-frequency intraseasonal oscillation near Ural Mountain in boreal summer. J. Climate. DOI: 10.1175/JCLI-D-20-0589.1
20. Yang, J* , SC He, Q Bao 2021 : Convective/large-scale rainfall partitions of tropical heavy precipitation in CMIP6 atmospheric models. Adv. Atm. Sci. doi: 10.1007/s00376-021-0238-4
21. Yang, J*, T. Zhu, H Lin, B Wang, Q Bao 2018 : Late-July Barrier for Sub-seasonal Forecast of Summer Daily Maximum Temperature over Yangtze River Basin. Geophys. Res. Lett. , DOI: 10.1029/2018GL080963
22. Yang, J*, WC. Wang, GX Chen, X Qi, SY 2018 : I ntraseasonal variation of the black carbon aerosol concentration and its impact on atmospheric circulation over the southeastern Tibetan Plateau. JGR-atmosphere , DOI:10.1029/2018JD029013
23. Yang, J*, Q. Bao, B. Wang, D. Y. Gong, H. Z. He, M. N. Gao, 2017 : Characterizing two types of transient intraseasonal oscillations in the Eastern Tibetan Plateau summer rainfall. Climate Dyn. , DOI: 10.1007/s00382-016-3170-z
24. Yang, J*, Q. Bao, D. Y. Gong, B. Wang, H. Z. He, M. N. Gao, 2014 : Distinct quasi-biweekly features of the subtropical East Asian monsoon during early and late summers. Climate Dyn. , 42: 1469-1486
25. Yang, J* , Q. Bao, D. Y. Ji, D. Y. Gong, Zhang, Z. Y. Mao R, S. J. Kim, 2014 : Simulation and causes of Eastern Antarctica surface cooling related with ozone depletion during austral summer in FGOALS-s2. Adv. Atm. Sci. , 31: 1147-1156
26. Yang, J*, D.Y. Gong, W. S. Wang, M. Hu, R. Mao, 2012 : Extreme drought event of 2009/2010 over southwestern China. Meteorol. Atmos. Phys. , 115(3-4): 173-184
27. Yang, J*, Q. Bao, X. C. Wang, 2012 : Intensified eastward and northward propagation of tropical intraseasonal oscillation in global warming scenario. Adv. Atm. Sci. , doi:10.1007/s00376-012-1260-3.
28. Yang, J*, Q. Bao, X. C. Wang, T. J. Zhou, 2012 : The tropical intraseasonal oscillation in SAMIL coupled and uncoupled general circulation models. Adv. Atm. Sci. , 29(3): 529-543
29. Yang, J*, B. Wang, B. Wang, Q. Bao, 2010 : Biweekly and 21-30 day variabilities of the subtropical East Asian monsoon over the Lower reach of Yangtze River Basin. J. Climate , 23: 1146-1159
30. Yang, J*, D. Y. Gong, 2010 : Intensified depression of light rainover mountains in Eastern China during boreal summer. Clim. Change , 100(3-4):807-815.
31. Yang, J*, B. Wang, B. Wang, L. J. Li, 2009 : The East Asia-western North Pacific boreal summer intraseasonal oscillation simulated in GAMIL 1.1.1. Adv. Atmos. Sci. , 26(3): 480-492.
32. Yang, J*, B. Wang, B. Wang, 2008 : Anti-correlated intensity change of the quasi-biweekly and 30-50-day oscillations over the South China Sea. Geophys. Res. Lett. , 35, L16702, doi:10.1029/2008GL034449
33. JL Wang, & Yang J* et al. 2021: Dynamical and machine learning hybrid seasonal prediction of China summer rainfall, Journal of Meteorological Research, 35(4), 583-593
34. ZQ Luo & Yang J* et al. 2020 : Extreme hot days over three global mega-regions: historical fidelity and future projection. Atmosphere Science Letter 2020, DOI: 10.1002/asl.1003
35. SY Zhou & Yang J* et al. 2020 : An observational study of the effect of aerosols on diurnal variation of heavy rainfall and associated clouds over Beijing-Tianjin-Hebei. Atmos. Chem. Phys . 20, 1-19, 2020, DOI: 10.5194/acp-20-1-2020
36. X QI & Yang J* 2019 : Extended-range prediction of a heat wave event over the Yangtze River Valley: role of intraseasonal signals, Atmospheric and Oceanic Science Letters, DOI: 10.1080/16742834.2019.1669408
37. X Qi & Yang J* et al, 2019 : Effects of the tropical/extratropical intraseasonal oscillations on generating the heat wave over Yangtze River Valley: a numerical study. Journal of Geophysical Research: Atmospheres. DOI: 10.1029/2018JD029868
38. MN Gao , Yang, J *, et al., 2018 : Footprints of Atlantic Multi-decadal Oscillation in the Low-frequency Variation of Extreme High Temperature in the Northern Hemisphere . J. Climate. DOI: DOI: 10.1175/JCLI-D-18-0446.1
39. SC He , Yang, J *, et al., 2018 : : Fidelity of the observational/reanalysis datasets and global climate models in representation of extreme precipitation in East China . J. Climate. DOI: 10.1175/JCLI-D-18-0104.1
40. SY . Zhou, Yang, J* et al. 2018 : Shift of daily rainfall peaks over the Beijing-Tianjin-Hebei region: An in diction of pollutant effects ? Int. J. Climatol. , DOI: 10.1002/joc5700
41. MN. Gao, B Wang, Yang, J* , et al. 2017 : Are peak summer sultry heat wave days over Yangtze-Huaihe River Basin predictable? Journal of Clim. , DOI: 10.1175/JCLI-D-17-0342.1
42. MN. Gao, Yang, J* , B Wang, et al. 2017 : How are heat waves over Yantze River valley associated with atmospheric quasi-biweekly oscillation? Climate Dyn . , DOI:10.1007/s00382-017-3526-z
43. HZ. He, Yang, J* , L. G. Wu, D. Y. Gong, B. Wang, M. N. Gao, 2016 : Unusual growth in intense typhoon occurrences over the Philippine Sea in September after the mid-2000s. Climate Dyn . , DOI: 10.1007/s00382-016-3181-9
44. MN. Gao, Yang, J* , D. Y. Gong, H. Z. He, S.-J. Kim, 2015 : Spring Arctic Oscillation-western North Pacific connection in CMIP5 models. Int. J. Climatol. , DOI: 10.1002/joc.4486.
45. HZ. He, Yang, J* , D. Y. Gong, R. Mao, Y. Q. Wang, M. N. Gao, 2015 : Decadal changes in Tropical Cyclone Activity over the western North Pacific in the late 1990s. Climate Dyn . , DOI: 10.1007/s00382-015-2541-1
46. MN. Gao, Yang, J* , D. Y. Gong, 2014 : Unstable relationship between spring Arctic Oscillation and East Asian summer monsoon. Int. J. Climatol. , 34: 2522-2528
47. HB. Liu, Yang, J*, D. L. Zhang, B. Wang, 2013 : Roles of Synoptic to Quasi-Biweekly Disturbances in Generating the Summer 2003 Heavy Rainfall in East China. Mon. Wea. Rev. , 142(2): 886–904
48. Q, Bao., Yang, J*, G. X. Wu, Y. M. Liu, B. Wang, 2010 : Roles of Anomalous Tibetan Plateau Warming on the Severe 2008 Winter Storm in Central-Southern China. Mon. Wea. Rev. , 138(6): 2375–2384
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