Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble

Abstract

Extreme precipitation is the leading cause of the flood, soil erosion, and drought with significant socioeconomic impacts on human resources. Therefore, projecting future precipitation changes, especially the intensity of extreme precipitation (IEP) in the future, is very important. This study’s main objective is IEP projection in Iran based on CMIP6 bias-correction (BC) multi-model ensemble (MME). The daily precipitation data of five CMIP6 BC models with 0.5 ͦ horizontal resolution was used under three shared socioeconomic pathways; SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Simple Daily Intensity (SDII) and maximum consecutive 1-day precipitation (RX1day) were used to measure IEP changes. Two Kling Gupta efficiency (KGE) and percent bias (PBIAS) methods evaluate the performance of the models, and the independence weighted mean (IWM) method was applied for ensemble averaging. Among the studied CMIP6 bias-correction models, the IPSL-CM6A-LR model has more underestimation than other models with a KGE score of 0.751 has the lowest performance and the MPI-ESM1-2-HR model (0.768) showed the highest performance. In general, the study results showed the uncertainties in CMIP6 models for precipitation, according to which no single model is reliable even in the BC method. The PBIAS in the region affected by Asian summer monsoon (ASM) in Iran is about 10%, based on which the bias of the mentioned models for monsoon precipitation is high. SDII and RX1day anomalies in all climatic zones of Iran except for the RX1day index in Cfa climatic zones in other zones and scenarios during the two periods 2021-2060 and 2061-2100 are positive. Also, the trend and slope of the IEP are increasing in all zones except for BWh, BWk, and Cfa zones for SSP1-2.6 and SSP3-7.0 scenarios. Investigation of trend IEP changes showed that the maximum of these changes will occur in the BWh climate zone, and the minimum would occur in the cold and mountainous climate zone of Iran (Dsc).

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Abbasian M, Moghim S, Abrishamchi A (2019) Performance of the general circulation models in simulating temperature and precipitation over Iran. Theor Appl Climatol 135(3-4):1465–1483

    Article  Google Scholar 

  2. Agel L, Barlow M, Polonia J, Coe D (2020) Simulation of northeast US extreme precipitation and its associated circulation by CMIP5 models. J Clim 33(22):9817–9834

    Article  Google Scholar 

  3. Ahmadi H, Rostami N, Dadashi-roudbari A (2020) Projected climate change in the Karkheh Basin, Iran, based on CORDEX models. Theor Appl Climatol 142(1):661–673

    Article  Google Scholar 

  4. Akinsanola AA, Zhou W (2019) Projections of West African summer monsoon rainfall extremes from two CORDEX models. Clim Dyn 52(3-4):2017–2028

    Article  Google Scholar 

  5. Akinsanola AA, Kooperman GJ, Pendergrass AG, Hannah WM, Reed KA (2020) Seasonal representation of extreme precipitation indices over the United States in CMIP6 present-day simulations. Environ Res Lett 15(9):094003

  6. Azari M, Oliaye A, Nearing MA (2021) Expected climate change impacts on rainfall erosivity over Iran based on CMIP5 climate models. J Hydrol 593:125826

    Article  Google Scholar 

  7. Bador M, Donat MG, Geoffroy O, Alexander LV (2018) Assessing the robustness of future extreme precipitation intensification in the CMIP5 ensemble. J Clim 31(16):6505–6525

    Article  Google Scholar 

  8. Bai H, Xiao D, Wang B, Liu DL, Feng P, Tang J (2020) Multi-model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China plain. Int J Climatol

  9. Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF (2018) Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5(1):1–12

    Article  Google Scholar 

  10. Bishop CH, Abramowitz G (2013) Climate model dependence and the replicate Earth paradigm. Clim Dyn 41(3-4):885–900

    Article  Google Scholar 

  11. Boucher O, Servonnat J, Albright AL, Aumont O, Balkanski Y, Bastrikov V et al (2020) Presentation and evaluation of the IPSL-CM6A-LR climate model. J Adv Model Earth Syst 12(7):e2019MS002010

    Article  Google Scholar 

  12. Caloiero T, Coscarelli R, Ferrari E, Mancini M (2011) Trend detection of annual and seasonal rainfall in Calabria (Southern Italy). Int J Climatol 31(1):44–56

    Article  Google Scholar 

  13. Carpenter SR, Booth EG, Kucharik CJ (2018) Extreme precipitation and phosphorus loads from two agricultural watersheds. Limnol Oceanogr 63(3):1221–1233

    Article  Google Scholar 

  14. Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?–arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250

    Article  Google Scholar 

  15. Cucchi M, Weedon GP, Amici A, Bellouin N, Lange S, Müller Schmied H, Hersbach H, Buontempo C (2020) WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst Sci Data 12(3):2097–2120

    Article  Google Scholar 

  16. Dadashi-Roudbari A, Ahmadi M (2020) Evaluating temporal and spatial variability and trend of aerosol optical depth (550 nm) over Iran using data from MODIS on board the Terra and Aqua satellites. Arab J Geosci 13(6):1–23

    Article  Google Scholar 

  17. Darand M (2020) Projected changes in extreme precipitation events over Iran in the 21st century based on CMIP5 models. Clim Res 82:75–95

    Article  Google Scholar 

  18. Davini P, D’Andrea F (2016) Northern hemisphere atmospheric blocking representation in global climate models: twenty years of improvements? J Clim 29(24):8823–8840

    Article  Google Scholar 

  19. Deng Z, Qiu X, Liu J, Madras N, Wang X, Zhu H (2016) Trend in frequency of extreme precipitation events over Ontario from ensembles of multiple GCMs. Clim Dyn 46(9-10):2909–2921

    Article  Google Scholar 

  20. Donat MG, Alexander LV, Yang H, Durre I, Vose R, Dunn RJH et al (2013) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. J Geophys Res-Atmos 118(5):2098–2118

    Article  Google Scholar 

  21. Doulabian S, Golian S, Toosi AS, Murphy C (2020) Evaluating the effects of climate change on precipitation and temperature for Iran using RCP scenarios. J Water Clim Chang 11(S1):1–19

  22. Dunn RJ, Alexander LV, Donat MG, Zhang X, Bador M, Herold N et al (2020) Development of an updated global land in situ-based data set of temperature and precipitation extremes: HadEX3. J Geophys Res-Atmos 125(16):e2019JD032263

    Article  Google Scholar 

  23. Evans JP (2009) 21st century climate change in the Middle East. Clim Chang 92(3-4):417–432

    Article  Google Scholar 

  24. Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9(5):1937–1958

    Article  Google Scholar 

  25. Fallah-Ghalhari G, Shakeri F, Dadashi-Roudbari A (2019) Impacts of climate changes on the maximum and minimum temperature in Iran. Theor Appl Climatol 138(3-4):1539–1562

    Article  Google Scholar 

  26. Farrokhi A, Abrishamchi A (2009) Detection of streamflow trends and variability in Karun River, Iran as parts of climate change and climate variability. In: World Environmental and Water Resources Congress 2009: Great Rivers, pp 1–12

    Google Scholar 

  27. Francis JA, Vavrus SJ (2012) Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophys Res Lett 39(6)1–6

  28. Garzanti E, Al-Juboury AI, Zoleikhaei Y, Vermeesch P, Jotheri J, Akkoca DB et al (2016) The Euphrates-Tigris-Karun river system: provenance, recycling and dispersal of quartz-poor foreland-basin sediments in arid climate. Earth Sci Rev 162:107–128

    Article  Google Scholar 

  29. Gentilucci M, Barbieri M, D’Aprile F, Zardi D (2020) Analysis of extreme precipitation indices in the Marche region (central Italy), combined with the assessment of energy implications and hydrogeological risk. Energy Rep 6:804–810

    Article  Google Scholar 

  30. Ghanavati E, Firouzabadi PZ, Jangi AA, Khosravi S (2008) Monitoring geomorphologic changes using Landsat TM and ETM+ data in the Hendijan River delta, southwest Iran. Int J Remote Sens 29(4):945–959

    Article  Google Scholar 

  31. Grose MR, Narsey S, Delage FP, Dowdy AJ, Bador M, Boschat G, Power S (2020) Insights from CMIP6 for Australia’s future climate. Earth’s Future 8(5):e2019EF001469

  32. Gusain A, Ghosh S, Karmakar S (2020) Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall. Atmos Res 232:104680

  33. Han J, Miao C, Duan Q, Wu J, Lei X, Liao W (2020) Variations in start date, end date, frequency and intensity of yearly temperature extremes across China during the period 1961–2017. Environ Res Lett 15(4):045007

    Article  Google Scholar 

  34. Huang DQ, Zhu J, Zhang YC, Huang AN (2013) Uncertainties on the simulated summer precipitation over Eastern China from the CMIP5 models. J Geophys Res-Atmos 118(16):9035–9047

    Article  Google Scholar 

  35. Ines AV, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138(1-4):44–53

    Article  Google Scholar 

  36. IPCC (2013) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate Change 2013 The physical science basis. Cambridge University Press, Cambridge

    Google Scholar 

  37. Iqbal Z, Shahid S, Ahmed K, Ismail T, Nawaz N (2019) Spatial distribution of the trends in precipitation and precipitation extremes in the sub-Himalayan region of Pakistan. Theor Appl Climatol 137(3-4):2755–2769

    Article  Google Scholar 

  38. Islamic Republic News Agency (2019) https://www.irna.ir/news/83251562/. Received on 2020-09-6. (In Persian)

  39. Jiang Z, Li W, Xu J, Li L (2015) Extreme precipitation indices over China in CMIP5 models. Part I: model evaluation. J Clim 28(21):8603–8619

    Article  Google Scholar 

  40. Jiang D, Hu D, Tian Z, Lang X (2020) Differences between CMIP6 and CMIP5 models in simulating climate over China and the East Asian monsoon. Adv Atmos Sci 37(10):1102–1118

    Article  Google Scholar 

  41. Katiraie-Boroujerdy PS, Akbari Asanjan A, Chavoshian A, Hsu KL, Sorooshian S (2019) Assessment of seven CMIP5 model precipitation extremes over Iran based on a satellite-based climate data set. Int J Climatol 39(8):3505–3522

    Article  Google Scholar 

  42. Kharin VV, Zwiers FW, Zhang X, Wehner M (2013) Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim Chang 119(2):345–357

    Article  Google Scholar 

  43. Knapp AK, Beier C, Briske DD, Classen AT, Luo Y, Reichstein M et al (2008) Consequences of more extreme precipitation regimes for terrestrial ecosystems. Bioscience 58(9):811–821

    Article  Google Scholar 

  44. Knoben WJ, Freer JE, Woods RA (2019) Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores. Hydrol Earth Syst Sci 23(10):4323–4331

    Article  Google Scholar 

  45. Li Z, Liu WZ, Zhang XC, Zheng FL (2009) Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. J Hydrol 377(1-2):35–42

    Article  Google Scholar 

  46. Lin J, Guan Q, Tian J, Wang Q, Tan Z, Li Z, Wang N (2020) Assessing temporal trends of soil erosion and sediment redistribution in the Hexi Corridor region using the integrated RUSLE-TLSD model. Catena 195:104756

    Article  Google Scholar 

  47. Liu J, Du H, Wu Z, He HS, Wang L, Zong S (2017) Recent and future changes in the combination of annual temperature and precipitation throughout China. Int J Climatol 37(2):821–833

    Article  Google Scholar 

  48. Madsen H, Lawrence D, Lang M, Martinkova M, Kjeldsen TR (2014) Review of trend analysis and climate change projections of extreme precipitation and floods in Europe. J Hydrol 519:3634–3650

    Article  Google Scholar 

  49. Maghrabi AH, Alotaibi RN (2018) Long-term variations of AOD from an AERONET station in the central Arabian Peninsula. Theor Appl Climatol 134(3-4):1015–1026

    Article  Google Scholar 

  50. Miri M, Samakosh JM, Raziei T, Jalilian A, Mahmodi M (2021) Spatial and temporal variability of temperature in Iran for the twenty-first century foreseen by the CMIP5 GCM models. Pure Appl Geophys 178(1):169–184

  51. Monjo R, Gaitán E, Pórtoles J, Ribalaygua J, Torres L (2016) Changes in extreme precipitation over Spain using statistical downscaling of CMIP5 projections. Int J Climatol 36(2):757–769

    Article  Google Scholar 

  52. Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463(7282):747–756

    Article  Google Scholar 

  53. Motiee H, McBean E, Motiee AR, Majdzadeh Tabatabaei MR (2020) Assessment of climate change under CMIP5-RCP scenarios on downstream rivers glaciers–Sardabrud River of Alam-Kuh glacier, Iran. Int J River Basin Manag 18(1):39–47

    Article  Google Scholar 

  54. Müller WA, Jungclaus JH, Mauritsen T, Baehr J, Bittner M, Budich R et al (2018) A higher-resolution version of the Max Planck Institute Earth System Model (MPI-ESM1. 2-HR). J Adv Model Earth Syst 10(7):1383–1413

    Article  Google Scholar 

  55. Naderi M (2020) Extreme climate events under global warming in northern Fars Province, southern Iran. Theor Appl Climatol 142(3):1221–1243

    Article  Google Scholar 

  56. Norris J, Chen G, Neelin JD (2019) Thermodynamic versus dynamic controls on extreme precipitation in a warming climate from the Community Earth System Model Large Ensemble. J Clim 32(4):1025–1045

    Article  Google Scholar 

  57. Odoulami RC, Akinsanola AA (2018) Recent assessment of West African summer monsoon daily rainfall trends. Weather 73(9):283–287

  58. O’Neill BC, Kriegler E, Riahi K, Ebi KL, Hallegatte S, Carter TR, Mathur R, van Vuuren DP (2014) A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim Chang 122(3):387–400

    Article  Google Scholar 

  59. Papalexiou SM, Montanari A (2019) Global and regional increase of precipitation extremes under global warming. Water Resour Res 55(6):4901–4914

    Google Scholar 

  60. Prein AF, Rasmussen RM, Ikeda K, Liu C, Clark MP, Holland GJ (2017) The future intensification of hourly precipitation extremes. Nat Clim Chang 7(1):48–52

    Article  Google Scholar 

  61. Rahimi J, Laux P, Khalili A (2020) Assessment of climate change over Iran: CMIP5 results and their presentation in terms of Köppen–Geiger climate zones. Theor Appl Climatol 141(1):183–199

  62. Rajczak J, Schär C (2017) Projections of future precipitation extremes over Europe: a multimodel assessment of climate simulations. J Geophys Res-Atmos 122(20):10–773

    Article  Google Scholar 

  63. Raziei T, Mofidi A, Santos JA, Bordi I (2012) Spatial patterns and regimes of daily precipitation in Iran in relation to large-scale atmospheric circulation. Int J Climatol 32(8):1226–1237

    Article  Google Scholar 

  64. Riahi K, Van Vuuren DP, Kriegler E, Edmonds J, O’neill BC, Fujimori S et al (2017) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob Environ Chang 42:153–168

    Article  Google Scholar 

  65. Roering JJ, Schmidt KM, Stock JD, Dietrich WE, Montgomery DR (2003) Shallow landsliding, root reinforcement, and the spatial distribution of trees in the Oregon Coast Range. Can Geotech J 40(2):237–253

    Article  Google Scholar 

  66. Salehnia N, Farid A, Hosseini F, Kolsoumi S, Zarrin A, Hasheminia M (2019) Comparing the performance of dynamical and statistical downscaling on historical run precipitation data over the semi-arid region. Asia-Pac J Atmos Sci 55(4):737–749. https://doi.org/10.1007/s13143-019-00112-1

    Article  Google Scholar 

  67. Samuels R, Hochman A, Baharad A, Givati A, Levi Y, Yosef Y, Saaroni H, Ziv B, Harpaz T, Alpert P (2018) Evaluation and projection of extreme precipitation indices in the Eastern Mediterranean based on CMIP5 multi-model ensemble. Int J Climatol 38(5):2280–2297

    Article  Google Scholar 

  68. Scoccimarro E, Gualdi S (2020) Heavy daily precipitation events in the CMIP6 worst-case scenario: projected twenty-first-century changes. J Clim 33(17):7631–7642

    Article  Google Scholar 

  69. Scoccimarro E, Villarini G, Vichi M, Zampieri M, Fogli PG, Bellucci A, Gualdi S (2015) Projected changes in intense precipitation over Europe at the daily and subdaily time scales. J Clim 28(15):6193–6203

    Article  Google Scholar 

  70. Sellar AA, Walton J, Jones CG, Wood R, Abraham NL, Andrejczuk M et al (2020) Implementation of UK Earth system models for CMIP6. J Adv Model Earth Syst 12(4):e2019MS001946

    Article  Google Scholar 

  71. Sentman LT, Dunne JP, Stouffer RJ, Krasting JP, Toggweiler JR, Broccoli AJ (2018) The mechanistic role of the Central American Seaway in a GFDL Earth System Model. Part 1: impacts on global ocean mean state and circulation. Paleoceanogr Paleoclimatol 33(7):840–859

    Article  Google Scholar 

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

    Article  Google Scholar 

  73. Sonkoué D, Monkam D, Fotso-Nguemo TC, Yepdo ZD, Vondou DA (2019) Evaluation and projected changes in daily rainfall characteristics over Central Africa based on a multi-model ensemble mean of CMIP5 simulations. Theor Appl Climatol 137(3-4):2167–2186

    Article  Google Scholar 

  74. Srivastava A, Grotjahn R, Ullrich PA (2020) Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions. Weather Clim Extremes 29:100268

    Article  Google Scholar 

  75. Stegall ST, Kunkel KE (2019) Simulation of daily extreme precipitation over the United States in the CMIP5 30-Yr decadal prediction experiment. J Appl Meteorol Climatol 58(4):875–886

    Article  Google Scholar 

  76. Takahashi HG, Kamizawa N, Nasuno T, Yamada Y, Kodama C, Sugimoto S, Satoh M (2020) Response of the Asian summer monsoon precipitation to global warming in a high-resolution global nonhydrostatic model. J Clim 33(18):8147–8164

    Article  Google Scholar 

  77. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498

  78. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A Math Phys Eng Sci 365(1857):2053–2075

    Article  Google Scholar 

  79. Trenberth KE, Jones PD, Ambenje P, Bojariu R, Easterling D, Klein Tank A, ..., Soden B (2007) Observations: surface and atmospheric climate change. Chapter 3. Climate change, 235-336

  80. Turner AG, Annamalai H (2012) Climate change and the South Asian summer monsoon. Nat Clim Chang 2(8):587–595

    Article  Google Scholar 

  81. Vaghefi SA, Keykhai M, Jahanbakhshi F, Sheikholeslami J, Ahmadi A, Yang H, Abbaspour KC (2019) The future of extreme climate in Iran. Sci Rep 9(1):1–11

    Article  Google Scholar 

  82. Wang HJ, Sun JQ, Chen HP, Zhu YL, Zhang Y, Jiang DB, Lang XM, Fan K, Yu ET, Yang S (2012) Extreme climate in China: facts, simulation and projection. Meteorol Z 21(3):279–304

    Article  Google Scholar 

  83. WMO (2019) WMO statement on the state of the global climate in 2018. World Meteorological Organization, Geneva

    Google Scholar 

  84. Wu C, Huang G (2016) Projection of climate extremes in the Zhujiang River basin using a regional climate model. Int J Climatol 36(3):1184–1196

  85. Xin X, Wu T, Zhang J, Yao J, Fang Y (2020) Comparison of CMIP6 and CMIP5 simulations of precipitation in China and the East Asian summer monsoon. Int J Climatol 40:6423–6440

    Article  Google Scholar 

  86. Xu Y, Xu C, Gao X, Luo Y (2009) Projected changes in temperature and precipitation extremes over the Yangtze River Basin of China in the 21st century. Quat Int 208(1-2):44–52

    Article  Google Scholar 

  87. Xu K, Xu B, Ju J, Wu C, Dai H, Hu BX (2019) Projection and uncertainty of precipitation extremes in the CMIP5 multimodel ensembles over nine major basins in China. Atmos Res 226:122–137

    Article  Google Scholar 

  88. Zamani R, Berndtsson R (2019) Evaluation of CMIP5 models for west and southwest Iran using TOPSIS-based method. Theor Appl Climatol 137(1-2):533–543

    Article  Google Scholar 

  89. Zamani Y, Monfared SAH, Hamidianpour M (2020) A comparison of CMIP6 and CMIP5 projections for precipitation to observational data: the case of Northeastern Iran. Theor Appl Climatol 142(3):1613–1623

    Article  Google Scholar 

  90. Zarenistanak M (2019) Historical trend analysis and future projections of precipitation from CMIP5 models in the Alborz mountain area, Iran. Meteorog Atmos Phys 131(5):1259–1280

    Article  Google Scholar 

  91. Zarrin A, Dadashi-Roudbari A (2020) Projection the long-term outlook Iran future temperature based on the output of the coupled model intercomparison project phase 6 (CMIP6). J Earth Space Phys 46(3):583–602. https://doi.org/10.22059/jesphys.2020.304870.1007226 (In Persian)

    Article  Google Scholar 

  92. Zhang X, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC et al (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip Rev Clim Chang 2(6):851–870

    Article  Google Scholar 

  93. Zhang W, Villarini G, Scoccimarro E, Vecchi GA (2017) Stronger influences of increased CO2 on subdaily precipitation extremes than at the daily scale. Geophys Res Lett 44(14):7464–7471

    Article  Google Scholar 

  94. Zhou B, Wen QH, Xu Y, Song L, Zhang X (2014) Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J Clim 27(17):6591–6611

    Article  Google Scholar 

Download references

Acknowledgments

This research was funded by Vice Chancellor for Research of Ferdowsi University of Mashhad, which is hereby acknowledged. We would like to thank the Iran Meteorological Organization (IRIMO) for providing the necessary data and information. We also acknowledge the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) and associated World Climate Research Program (WCRP) for the production of the data.

Funding

(Vice Chancellor for Research of Ferdowsi University of Mashhad).

Author information

Affiliations

Authors

Contributions

Conceived and designed the analysis: Azar Zarrin and Abbasali Dadashi-Roudbari. Collected the data: Azar Zarrin and Abbasali Dadashi-Roudbari. Contributed data or analysis tools: Azar Zarrin and Abbasali Dadashi-Roudbari. Performed the analysis: Azar Zarrin and Abbasali Dadashi-Roudbari. Wrote the paper: Abbasali Dadashi-Roudbari. Writing—review and editing: Azar Zarrin. Corresponding author: Azar Zarrin.

Corresponding author

Correspondence to Azar Zarrin.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zarrin, A., Dadashi-Roudbari, A. Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theor Appl Climatol (2021). https://doi.org/10.1007/s00704-021-03568-2

Download citation