Skip to main content

Advertisement

SpringerLink
Log in

Downscaling and uncertainty analysis of future concurrent long-duration dry and hot events in China

  • Published:
Climatic Change Aims and scope Submit manuscript

Abstract

Using fourteen global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) downscaled by four statistical downscaling methods, future changes and the associated uncertainty in concurrent long-duration dry and hot (LDDH) events are investigated in China during summer. The downscaling methods include BCSD (bias-correction and spatial downscaling), BCCI (bias-correction and climate imprint), BCCAQ (bias correction constructed analogues with quantile mapping reordering), and CDF-t (cumulative distribution function transform). The downscaling methods can efficiently improve the accuracy over the driving GCMs in terms of spatial variability, bias, and inter-annual variability of LDDH characteristics. Overall, the three quantile mapping based techniques (BCSD, BCCI, and BCCAQ) outperform CDF-t in simulating the spatial and temporal features of LDDH events. In the twenty-first century, all downscaling methods project a consistent increasing tendency for the frequency, magnitude, and total days of LDDH events over most parts of China, with higher increases under RCP8.5 compared to RCP4.5. A substantial increase in spatially contiguous regions simultaneously experiencing LDDH events is seen by mid-century under both scenarios. Changes in the frequency, magnitude, and total days of LDDH events are predicted with high confidence. For most indices, model uncertainty dominates throughout the century and does not change much over time. However, for the projection of temperature magnitude of LDDH events, the dominant role of GCM related uncertainty in the early twenty-first century declines as scenario uncertainty becomes more important towards the end of the century.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The data used in this study are available from the corresponding author upon reasonable request.

References

  • Abatzoglou JT, Brown TJ (2012) A comparison of statistical downscaling methods suited for wildfire applications. Int J Climatol 32(5):772–780

    Article  Google Scholar 

  • Adachi SA, Tomita H (2020) Methodology of the constraint condition in dynamical downscaling for regional climate evaluation: a review. J Geophys Res: Atmos 125(11):e2019JD032166. https://doi.org/10.1029/2019JD032166

    Article  Google Scholar 

  • AghaKouchak A, Chiang F, Huning LS, Love CA, Mallakpour I, Mazdiyasni O et al (2020) Climate extremes and compound hazards in a warming world. Annu Rev Earth Planet Sci 48:519–548

    Article  Google Scholar 

  • Ahmadalipour A, Moradkhani H, Rana A (2018) Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin. Clim Dyn 50(1–2):717–733

    Article  Google Scholar 

  • Alizadeh MR, Adamowski J, Nikoo MR, AghaKouchak A, Dennison P, Sadegh M (2020) A century of observations reveals increasing likelihood of continental-scale compound dry-hot extremes. Sci Adv 6(39):eaaz4571

    Article  Google Scholar 

  • Barriopedro D, Fischer EM, Luterbacher J, Trigo RM, García-Herrera R (2011) The hot summer of 2010: redrawing the temperature record map of Europe. Science 332(6026):220–224

    Article  Google Scholar 

  • Berg A, Lintner BR, Findell K, Seneviratne SI, van den Hurk B, Ducharne A et al (2015) Interannual coupling between summertime surface temperature and precipitation over land: Processes and implications for climate change. J Clim 28(3):1308–1328

    Article  Google Scholar 

  • Bevacqua E, Maraun D, Vousdoukas MI, Voukouvalas E, Vrac M, Mentaschi L, Widmann M (2019) Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change. Sci Adv 5(9):eaaw5531

    Article  Google Scholar 

  • Bosshard T, Carambia M, Goergen K, Kotlarski S, Krahe P, Zappa M, Schär C (2013) Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections. Water Resour Res 49(3):1523–1536

    Article  Google Scholar 

  • Bürger G, Sobie SR, Cannon AJ, Werner AT, Murdock TQ (2013) Downscaling extremes: an intercomparison of multiple methods for future climate. J Clim 26(10):3429–3449

    Article  Google Scholar 

  • Cannon AJ (2018) Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Clim Dyn 50(1):31–49

    Article  Google Scholar 

  • Casanueva A, Bedia J, Herrera S, Fernández J, Gutiérrez JM (2018) Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool. Clim Change 147(3):411–425

    Article  Google Scholar 

  • Chen S, Yuan X (2021) CMIP6 projects less frequent seasonal soil moisture droughts over China in response to different warming levels. Environ Res Lett 16(4):044053

    Article  Google Scholar 

  • Chen S, Yuan X (2022) Quantifying the uncertainty of internal variability in future projections of seasonal soil moisture droughts over China. Sci Total Environ 824:153817

    Article  Google Scholar 

  • Deser C, Phillips AS, Alexander MA, Smoliak BV (2014) Projecting North American climate over the next 50 years: uncertainty due to internal variability. J Clim 27(6):2271–2296

    Article  Google Scholar 

  • Dibike Y, Prowse T, Bonsal B, O’Neil H (2017) Implications of future climate on water availability in the western Canadian river basins. Int J Climatol 37(7):3247–3263

    Article  Google Scholar 

  • Dong T, Dong W (2021) Evaluation of extreme precipitation over Asia in CMIP6 models. Clim Dyn 57(7):1751–1769

    Article  Google Scholar 

  • Donnelly C, Greuell W, Andersson J, Gerten D, Pisacane G, Roudier P, Ludwig F (2017) Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Climatic Change 143(1):13–26

    Article  Google Scholar 

  • Dosio A, Fischer EM (2018) Will half a degree make a difference? Robust projections of indices of mean and extreme climate in Europe under 1.5ºC, 2ºC, and 3ºC global warming. Geophys Res Lett 45(2):935–944

    Article  Google Scholar 

  • Eum HI, Cannon AJ (2017) Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the CMIP5 ensemble. Int J Climatol 37(8):3381–3397

    Article  Google Scholar 

  • Eum HI, Dibike Y, Prowse T (2017) Climate-induced alteration of hydrologic indicators in the Athabasca River Basin, Alberta, Canada. J Hydrol 544:327–342

    Article  Google Scholar 

  • Evin G, Hingray B, Blanchet J, Eckert N, Morin S, Verfaillie D (2019) Partitioning uncertainty components of an incomplete ensemble of climate projections using data augmentation. J Clim 32(8):2423–2440

  • Foley AM (2010) Uncertainty in regional climate modelling: a review. Prog Phys Geogr 34(5):647–670

    Article  Google Scholar 

  • François B, Vrac M, Cannon AJ, Robin Y, Allard D (2020) Multivariate bias corrections of climate simulations: which benefits for which losses? Earth Syst Dynamics 11(2):537–562

    Article  Google Scholar 

  • Gao C, Booij MJ, Xu YP (2020) Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability. Hydrol Earth Syst Sci 24(6):3251–3269

    Article  Google Scholar 

  • Gebrechorkos SH, Hülsmann S, Bernhofer C (2019) Regional climate projections for impact assessment studies in East Africa. Environ Res Lett 14(4):044031

    Article  Google Scholar 

  • Gobiet A, Kotlarski S, Beniston M, Heinrich G, Rajczak J, Stoffel M (2014) 21st century climate change in the European Alps—a review. Sci Total Environ 493:1138–1151

    Article  Google Scholar 

  • Gutmann E, Pruitt T, Clark MP, Brekke L, Arnold JR, Raff DA, Rasmussen RM (2014) An intercomparison of statistical downscaling methods used for water resource assessments in the United States. Water Resour Res 50(9):7167–7186

    Article  Google Scholar 

  • Hao Z, Hao F, Singh VP, Zhang X (2018) Changes in the severity of compound drought and hot extremes over global land areas. Environ Res Lett 13(12):124022

    Article  Google Scholar 

  • Hattermann FF, Vetter T, Breuer L, Su B, Daggupati P, Donnelly C et al (2018) Sources of uncertainty in hydrological climate impact assessment: a cross-scale study. Environ Res Lett 13(1):015006

    Article  Google Scholar 

  • Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteor Soc 90(8):1095–1108

    Article  Google Scholar 

  • Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37(1–2):407–418

    Article  Google Scholar 

  • Hui P, Tang J, Wang S, Niu X, Zong P, Dong X (2018) Climate change projections over China using regional climate models forced by two CMIP5 global models. Part II: projections of future climate. Int J Climatol 38:e78–e94

    Article  Google Scholar 

  • IPCC (2021) Summary for Policymakers. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press/UNEP

  • Joetzjer E, Douville H, Delire C, Ciais P (2013) Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3. Clim Dyn 41(11):2921–2936

    Article  Google Scholar 

  • Kay AL, Davies HN, Bell VA, Jones RG (2009) Comparison of uncertainty sources for climate change impacts: flood frequency in England. Clim Change 92(1):41–63

    Article  Google Scholar 

  • Kendon EJ, Ban N, Roberts NM, Fowler HJ, Roberts MJ, Chan SC et al (2017) Do convection-permitting regional climate models improve projections of future precipitation change? Bull Am Meteor Soc 98(1):79–93

    Article  Google Scholar 

  • Kirtman B, Power SB, Adedoyin AJ, Boer GJ, Bojariu R, Camilloni I et al (2013) Near-term climate change: projections and predictability. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midggley PM (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp 953–1028

    Google Scholar 

  • Lanzante JR, Nath MJ, Whitlock CE, Dixon KW, Adams-Smith D (2019) Evaluation and improvement of tail behaviour in the cumulative distribution function transform downscaling method. Int J Climatol 39(4):2449–2460

    Article  Google Scholar 

  • Lavaysse C, Vrac M, Drobinski P, Lengaigne M, Vischel T (2012) Statistical downscaling of the French Mediterranean climate: assessment for present and projection in an anthropogenic scenario. Nat Hazard 12(3):651–670

    Article  Google Scholar 

  • Le Roux R, Katurji M, Zawar-Reza P, Quénol H, Sturman A (2018) Comparison of statistical and dynamical downscaling results from the WRF model. Environ Model Softw 100:67–73

    Article  Google Scholar 

  • Lehner F, Deser C, Maher N, Marotzke J, Fischer EM, Brunner L et al (2020) Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth Syst Dynam 11(2):491–508

    Article  Google Scholar 

  • Leonard M, Westra S, Phatak A, Lambert M, van den Hurk B, McInnes K et al (2014) A compound event framework for understanding extreme impacts. Wiley Interdisc Rev: Climate Change 5(1):113–128

    Google Scholar 

  • Liu X, He B, Guo L, Huang L, Chen D (2020) Similarities and differences in the mechanisms causing the European summer heatwaves in 2003, 2010, and 2018. Earth’s Future 8(4):e2019EF001386

    Article  Google Scholar 

  • Lu Y, Hu H, Li C, Tian F (2018) Increasing compound events of extreme hot and dry days during growing seasons of wheat and maize in China. Sci Rep 8(1):1–8

    Article  Google Scholar 

  • Lyon B, Barnston AG, Coffel E, Horton RM (2019) Projected increase in the spatial extent of contiguous US summer heat waves and associated attributes. Environ Res Lett 14(11):114029

    Article  Google Scholar 

  • Mani A, Tsai FTC (2017) Ensemble averaging methods for quantifying uncertainty sources in modeling climate change impact on runoff projection. J Hydrol Eng 22(4):04016067

    Article  Google Scholar 

  • Manning C, Widmann M, Bevacqua E, Van Loon AF, Maraun D, Vrac M (2019) Increased probability of compound long-duration dry and hot events in Europe during summer (1950–2013). Environ Res Lett 14(9):094006

    Article  Google Scholar 

  • Maraun D, Widmann M (2018) Statistical downscaling and bias correction for climate research. Cambridge University Press

    Book  Google Scholar 

  • Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M et al (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3). https://doi.org/10.1029/2009RG000314

  • Maraun D, Widmann M, Gutiérrez JM, Kotlarski S, Chandler RE, Hertig E et al (2015) VALUE: a framework to validate downscaling approaches for climate change studies. Earth’s Future 3(1):1–14

    Article  Google Scholar 

  • Maraun D, Huth R, Gutiérrez JM, Martín DS, Dubrovsky M, Fischer A et al (2019) The VALUE perfect predictor experiment: evaluation of temporal variability. Int J Climatol 39(9):3786–3818

    Article  Google Scholar 

  • Mazdiyasni O, AghaKouchak A (2015) Substantial increase in concurrent droughts and heatwaves in the United States. Proc Natl Acad Sci 112(37):11484–11489

    Article  Google Scholar 

  • McSweeney CF, Jones RG, Lee RW, Rowell DP (2015) Selecting CMIP5 GCMs for downscaling over multiple regions. Clim Dyn 44(11):3237–3260

    Article  Google Scholar 

  • Messori G, Bevacqua E, Caballero R, Coumou D, De Luca P, Faranda D et al (2021) Compound climate events and extremes in the midlatitudes: dynamics, simulation, and statistical characterization. Bull Am Meteor Soc 102(4):E774–E781

    Article  Google Scholar 

  • Moges E, Demissie Y, Larsen L, Yassin F (2021) Sources of hydrological model uncertainties and advances in their analysis. Water 13(1):28

    Article  Google Scholar 

  • National Academies of Sciences, Engineering, and Medicine (2016) Attribution of extreme weather events in the context of climate change. The National Academies Press, Washington, DC. https://doi.org/10.17226/21852

  • Panofsky HA, Brier GW (1968) Some applications of statistics to meteorology. University Park Penn, State University, College of Earth and Mineral Sciences

  • Pechlivanidis IG, Jackson BM, Mcintyre NR, Wheater HS (2011) Catchment scale hydrological modelling: a review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications. Global NEST J 13(3):193–214

    Google Scholar 

  • Perkins SE, Pitman AJ, Holbrook NJ, McAneney J (2007) Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J Clim 20:4356–4376

    Article  Google Scholar 

  • Pfleiderer P, Schleussner CF, Kornhuber K, Coumou D (2019) Summer weather becomes more persistent in a 2 ℃ world. Nat Clim Chang 9(9):666–671

    Article  Google Scholar 

  • Pierce DW, Das T, Cayan DR, Maurer EP, Miller NL, Bao Y et al (2013) Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling. Clim Dyn 40(3–4):839–856

    Article  Google Scholar 

  • Ren FM, Trewin B, Brunet M, Dushmanta P, Walter A, Baddour O, Korber M (2018) A research progress review on regional extreme events. Adv Clim Chang Res 9(3):161–169

    Article  Google Scholar 

  • Ridder NN, Pitman AJ, Westra S, Ukkola A, Do HX, Bador M et al (2020) Global hotspots for the occurrence of compound events. Nat Commun 11(1):1–10

    Google Scholar 

  • Ridder NN, Pitman AJ, Ukkola AM (2021) Do CMIP6 climate models simulate global or regional compound events skillfully? Geophys Res Lett 48(2):e2020GL091152

    Article  Google Scholar 

  • Sarhadi A, Ausín MC, Wiper MP, Touma D, Diffenbaugh NS (2018) Multidimensional risk in a nonstationary climate: joint probability of increasingly severe warm and dry conditions. Science advances 4(11):eaau3487

    Article  Google Scholar 

  • Schuldt B, Buras A, Arend M, Vitasse Y, Beierkuhnlein C, Damm A et al (2020) A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl Ecol 45:86–103

    Article  Google Scholar 

  • Sedlmeier K, Feldmann H, Schädler G (2018) Compound summer temperature and precipitation extremes over central Europe. Theoret Appl Climatol 131(3):1493–1501

    Article  Google Scholar 

  • Serinaldi F (2015) Dismissing return periods! Stoch Env Res Risk Assess 29(4):1179–1189

    Article  Google Scholar 

  • Sheffield J, Andreadis KM, Wood EF, Lettenmaier DP (2009) Global and continental drought in the second half of the twentieth century: severity-area-duration analysis and temporal variability of large-scale events. J Clim 22(8):1962–1981

    Article  Google Scholar 

  • Sillmann J, Croci-Maspoli M (2009) Present and future atmospheric blocking and its impact on European mean and extreme climate. Geophys Res Lett 36(L10702). https://doi.org/10.1029/2009GL038259

  • Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013a) Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J Geophys Res: Atmos 118(4):1716–1733

    Article  Google Scholar 

  • Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D (2013b) Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J Geophys Res: Atmos 118(6):2473–2493

    Article  Google Scholar 

  • Sunyer Pinya MA, Hundecha Y, Lawrence D, Madsen H, Willems P, Martinkova M et al (2015) Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe. Hydrol Earth Syst Sci 19(4):1827–1847

    Article  Google Scholar 

  • Tang J, Niu X, Wang S, Gao H, Wang X, Wu J (2016) Statistical downscaling and dynamical downscaling of regional climate in China: present climate evaluations and future climate projections. J Geophys Res: Atmos 121(5):2110–2129

    Article  Google Scholar 

  • Vautard R, Noël T, Li L, Vrac M, Martin E, Dandin P et al (2013) Climate variability and trends in downscaled high-resolution simulations and projections over Metropolitan France. Clim Dyn 41(5):1419–1437

    Article  Google Scholar 

  • Vigaud N, Vrac M, Caballero Y (2013) Probabilistic downscaling of GCM scenarios over southern India. Int J Climatol 33(5):1248–1263

    Article  Google Scholar 

  • Werner AT, Cannon AJ (2016) Hydrologic extremes–an intercomparison of multiple gridded statistical downscaling methods. Hydrol Earth Syst Sci 20(4):1483–1508

    Article  Google Scholar 

  • Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resources Research 42(W02419). https://doi.org/10.1029/2005WR004065

  • Wilcke RAI, Mendlik T, Gobiet A (2013) Multi-variable error correction of regional climate models. Clim Change 120(4):871–887

    Article  Google Scholar 

  • Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res: Atmos 107(D20):ACL-6

    Google Scholar 

  • Woolway RI, Anderson EJ, Albergel C (2021) Rapidly expanding lake heatwaves under climate change. Environ Res Lett 16(9):094013

    Article  Google Scholar 

  • Wu J, Gao XJ (2013) A gridded daily observation dataset over China region and comparison with the other datasets (in Chinese). Chin J Geophys 56(4):1102–1111

    Google Scholar 

  • Wu FT, Wang SY, Fu CB, Qian Y, Gao Y, Lee DK et al (2016) Evaluation and projection of summer extreme precipitation over East Asia in the Regional Model Inter-comparison Project. Climate Res 69(1):45–58

    Article  Google Scholar 

  • Wu X, Hao Z, Tang Q, Singh VP, Zhang X, Hao F (2021) Projected increase in compound dry and hot events over global land areas. Int J Climatol 41(1):393–403

    Article  Google Scholar 

  • Yang Y, Tang J, Wang S, Liu G (2018) Differential impacts of 1.5 and 2 ºC warming on extreme events over China using statistically downscaled and bias-corrected CESM low-warming experiment. Geophys Res Lett 45(18):9852–9860

    Article  Google Scholar 

  • Yang Y, Tang J, Xiong Z, Wang S, Yuan J (2019a) An intercomparison of multiple statistical downscaling methods for daily precipitation and temperature over China: present climate evaluations. Clim Dyn 53(7):4629–4649

    Article  Google Scholar 

  • Yang Y, Tang J, Xiong Z, Wang S, Yuan J (2019b) An intercomparison of multiple statistical downscaling methods for daily precipitation and temperature over China: future climate projections. Clim Dyn 52(11):6749–6771

    Article  Google Scholar 

  • Yang Y, Maraun D, Ossó A, Tang J (2022) Increased spatial extent and likelihood of compound long-duration dry and hot events in China, 1961–2014. Natural Hazards and Earth System Sciences Discussions 1–26. https://doi.org/10.5194/nhess-2022-194

  • Yip S, Ferro CA, Stephenson DB, Hawkins E (2011) A simple, coherent framework for partitioning uncertainty in climate predictions. J Clim 24(17):4634–4643

    Article  Google Scholar 

  • You Q, Wu F, Shen L, Pepin N, Jiang Z, Kang S (2020) Tibetan Plateau amplification of climate extremes under global warming of 1.5° C, 2° C and 3° C. Global Planet Chang 192:103261

    Article  Google Scholar 

  • Yu R, Zhai P (2020) More frequent and widespread persistent compound drought and heat event observed in China. Sci Rep 10(1):1–7

    Article  Google Scholar 

  • Zhou S, Williams AP, Berg AM, Cook BI, Zhang Y, Hagemann S et al (2019) Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc Natl Acad Sci 116(38):18848–18853

    Article  Google Scholar 

  • Zscheischler J, Fischer EM (2020) The record-breaking compound hot and dry 2018 growing season in Germany. Weather Climate Extremes 29:100270

    Article  Google Scholar 

  • Zscheischler J, Seneviratne SI (2017) Dependence of drivers affects risks associated with compound events. Sci Adv 3(6):e1700263

    Article  Google Scholar 

Download references

Funding

This work is supported by the National Key Research and Development Program of China (2018YFA0606003) and the Jiangsu Collaborative Innovation Center for Climate Change.

Author information

Authors and Affiliations

  1. Key Laboratory of Mesoscale Severe Weather/Ministry of Education, Nanjing University, Nanjing, 210023, China

    Yi Yang & Jianping Tang

  2. School of Atmospheric Sciences, Nanjing University, 163 Xianlin Road, Nanjing, 210023, China

    Yi Yang & Jianping Tang

Authors
  1. Yi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianping Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the research idea and study design. Y.Y. conducted all analyses and wrote the first draft of the manuscript. All authors discussed the results and contributed to writing of the final manuscript.

Corresponding author

Correspondence to Jianping Tang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1499 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Tang, J. Downscaling and uncertainty analysis of future concurrent long-duration dry and hot events in China. Climatic Change 176, 11 (2023). https://doi.org/10.1007/s10584-023-03481-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10584-023-03481-9

Keywords

Search

Navigation