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Observation-constrained projections reveal longer-than-expected dry spells

  • Seneviratne, S. I. et al. Weather and climate extreme events in a changing climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) 1513–1766 https://doi.org/10.1017/9781009157896.013 (Cambridge Univ. Press, 2021).

  • Almazroui, M. et al. Projected changes in climate extremes using CMIP6 simulations over SREX regions. Earth Syst. Environ. 5, 481–497 (2021).

    Article 

    Google Scholar
     

  • Orlowsky, B. & Seneviratne, S. I. Elusive drought: uncertainty in observed trends and short- and long-term CMIP5 projections. Hydrol. Earth Syst. Sci. 17, 1765–1781 (2013).

    Article 
    ADS 

    Google Scholar
     

  • Lu, J., Carbone, G. J. & Grego, J. M. Uncertainty and hotspots in 21st century projections of agricultural drought from CMIP5 models. Sci. Rep. 9, 4922 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vatter, J., Wagnitz, P., Schmiester J. & Hernandez, E. Drought Risk: The Global Thirst for Water in the Era of Climate Crisis (WWF Germany, 2019).

  • United Nations Office for Disaster Risk Reduction. Special Report on Drought 2021 (United Nations, 2021).

  • World Meteorological Organization. State of the Global Climate 2021 https://library.wmo.int/doc_num.php?explnum_id=11178 (WMO, 2022).

  • Yang, T., Ding, J., Liu, D., Wang, X. & Wang, T. Combined use of multiple drought indices for global assessment of dry gets drier and wet gets wetter paradigm. J. Clim. 32, 737–748 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Stephens, G. L. et al. Dreary state of precipitation in global models. J. Geophys. Res. Atmos. 115, https://doi.org/10.1029/2010JD014532 (2010).

  • Bastin, S. et al. Impact of humidity biases on light precipitation occurrence: observations versus simulations. Atmos. Chem. Phys. 19, 1471–1490 (2019).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Sun, Y., Solomon, S., Dai, A. & Portmann, R. W. How often will it rain? J. Clim. 20, 4801–4818 (2007).

    Article 
    ADS 

    Google Scholar
     

  • Nasrollahi, N. et al. How well do CMIP5 climate simulations replicate historical trends and patterns of meteorological droughts? Water Resour. Res. 51, 2847–2864 (2015).

    Article 
    ADS 

    Google Scholar
     

  • Trenberth, K. E., Dai, A., Rasmussen, R. M. & Parsons, D. B. The changing character of precipitation. Bull. Am. Meteorol. Soc. 84, 1205–1218 (2003).

    Article 
    ADS 

    Google Scholar
     

  • Vogel, M. M., Zscheischler, J. & Seneviratne, S. I. Varying soil moisture–atmosphere feedbacks explain divergent temperature extremes and precipitation projections in central Europe. Earth Syst. Dyn. 9, 1107–1125 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Hirota, N., Michibata, T., Shiogama, H., Ogura, T. & Suzuki, K. Impacts of precipitation modeling on cloud feedback in MIROC6. Geophys. Res. Lett. 49, e2021GL096523 (2022).

  • Orth, R., Zscheischler, J. & Seneviratne, S. I. Record dry summer in 2015 challenges precipitation projections in Central Europe. Sci. Rep. 6, 28334 (2016).

  • Herrera-Estrada, J. E., Satoh, Y. & Sheffield, J. Spatiotemporal dynamics of global drought. Geophys. Res. Lett. https://doi.org/10.1002/2016GL071768 (2017).

    Article 

    Google Scholar
     

  • Topál, D., Hatvani, I. G. & Kern, Z. Refining projected multidecadal hydroclimate uncertainty in East-Central Europe using CMIP5 and single-model large ensemble simulations. Theor. Appl. Climatol. 142, 1147–1167 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Zhang, S. & Chen, J. Uncertainty in projection of climate extremes: a comparison of CMIP5 and CMIP6. J. Meteorol. Res. 35, 646–662 (2021).

    Article 

    Google Scholar
     

  • Maraun, D. et al. Towards process-informed bias correction of climate change simulations. Nat. Clim. Change 7, 764–773 (2017).

    Article 

    Google Scholar
     

  • Kreibich, H. et al. The challenge of unprecedented floods and droughts in risk management. Nature 608, 80–86 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Brient, F. Reducing uncertainties in climate projections with emergent constraints: concepts, examples and prospects. Adv. Atmos. Sci. 37, 1–15 (2020).

    Article 

    Google Scholar
     

  • Hall, A., Cox, P., Huntingford, C. & Klein, S. Progressing emergent constraints on future climate change. Nat. Clim. Change 9, 269–278 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Taylor, I. H. et al. Contributions to uncertainty in projections of future drought under climate change scenarios. Hydrol. Earth Syst. Sci. Discuss. 9, 12613–12653 (2012).

    ADS 

    Google Scholar
     

  • Hausfather, Z. & Peters, G. P. Emissions – the ‘business as usual’ story is misleading. Nature 577, 618–620 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Abramowitz, G. & Pitman, A. J. Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation. Geophys. Res. Lett. 47, https://doi.org/10.1029/2020GL087820 (2020).

  • Wainwright, C. M., Allan, R. P. & Black, E. Consistent trends in dry spell length in recent observations and future projections. Geophys. Res. Lett. 49, https://doi.org/10.1029/2021GL097231 (2022).

  • Li, J., Huo, R., Chen, H., Zhao, Y. & Zhao, T. Comparative assessment and future prediction using CMIP6 and CMIP5 for annual precipitation and extreme precipitation simulation. Front. Earth Sci. 9, https://doi.org/10.3389/feart.2021.687976 (2021).

  • Kim, Y.-H., Min, S.-K., Zhang, X., Sillmann, J. & Sandstad, M. Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Weather Clim. Extrem. 29, https://doi.org/10.1016/j.wace.2020.100269 (2020).

  • Funk, C. et al. Exploring trends in wet-season precipitation and drought indices in wet, humid and dry regions. Environ. Res. Lett. 14, 115002 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Chen, D., Dai, A. & Hall, A. The convective-to-total precipitation ratio and the “drizzling” bias in climate models. J. Geophys. Res. Atmos. 126, https://doi.org/10.1029/2020JD034198 (2021).

  • Simpson, I. R. et al. Observed humidity trends in dry regions contradict climate models. Proc. Natl Acad. Sci. USA 121, e2302480120 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cox, P. M. et al. Amazonian forest dieback under climate-carbon cycle projections for the 21st century. Theor. Appl. Climatol. 78, 137–156 (2004).

    Article 
    ADS 

    Google Scholar
     

  • Monteverde, C., De Sales, F. & Jones, C. Evaluation of the CMIP6 performance in simulating precipitation in the Amazon river basin. Climate 10, https://doi.org/10.3390/cli10080122 (2022).

  • Baker, J. C. A. et al. Robust Amazon precipitation projections in climate models that capture realistic land–atmosphere interactions. Environ. Res. Lett. 16, 074002 (2021).

  • Tierney, J. E., Ummenhofer, C. C. & deMenocal, P. B. Past and future rainfall in the Horn of Africa. Sci. Adv. 1, e1500682 (2015).

  • Baxter, A. J. et al. Reversed Holocene temperature–moisture relationship in the Horn of Africa. Nature 620, 336–343 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Selten, F. M., Bintanja, R., Vautard, R. & van den Hurk, B. J. J. M. Future continental summer warming constrained by the present-day seasonal cycle of surface hydrology. Sci. Rep. 10, 4721 (2020).

  • Hirabayashi, Y., Tanoue, M., Sasaki, O., Zhou, X. & Yamazaki, D. Global exposure to flooding from the new CMIP6 climate model projections. Sci. Rep. 11, 3740 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • You, Q. et al. Recent frontiers of climate changes in East Asia at global warming of 1.5°C and 2°C. NPJ Clim. Atmos. Sci. 5, 80 (2022).

    Article 

    Google Scholar
     

  • Wang, Z., Duan, A., Yang, S. & Ullah, K. Atmospheric moisture budget and its regulation on the variability of summer precipitation over the Tibetan Plateau. J. Geophys. Res. Atmos. 122, 614–630 (2017).

    Article 
    ADS 

    Google Scholar
     

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

    Article 

    Google Scholar
     

  • Zhang, R., Chu, Q., Zuo, Z. & Qi, Y. Summertime moisture sources and transportation pathways for China and associated atmospheric circulation patterns. Front. Earth Sci. 9, https://doi.org/10.3389/feart.2021.756943 (2021).

  • Donat, M. G., Pitman, A. J. & Angélil, O. Understanding and reducing future uncertainty in midlatitude daily heat extremes via land surface feedback constraints. Geophys. Res. Lett. 45, 10,627–10,636 (2018).

    Article 

    Google Scholar
     

  • Terai, C., Caldwell, P. & Klein, S. Why do climate models drizzle too much and what impact does this have? In AGU Fall Meeting Proceedings https://agu.confex.com/agu/fm16/meetingapp.cgi/Paper/162370 (2016).

  • Herrera-Estrada, J. E. & Sheffield, J. Uncertainties in future projections of summer droughts and heat waves over the contiguous United States. J. Clim. 30, 6225–6246 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Wainwright, C. M. et al. ‘Eastern African paradox’ rainfall decline due to shorter not less intense long rains. NPJ Clim. Atmos. Sci. 2, 34 (2019).

  • Douville, H., Chadwick, R., Saint-Lu, M. & Medeiros, B. Drivers of dry day sensitivity to increased CO2. Geophys. Res. Lett. 50, https://doi.org/10.1029/2023GL103200 (2023).

  • Alexander, L. V. et al. Intercomparison of annual precipitation indices and extremes over global land areas from in situ, space-based and reanalysis products. Environ. Res. Lett. 15, 055002 (2020).

  • Zhang, X. et al. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip. Rev. Clim. Change 2, 851–870 (2011).

    Article 

    Google Scholar
     

  • Zhang, X. ETCCDI climate change indices. https://etccdi.pacificclimate.org/ (2020).

  • Donat, M. G. et al. Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. J. Geophys. Res. Atmos. https://doi.org/10.1002/jgrd.50150 (2013).

    Article 

    Google Scholar
     

  • Field, C. B. et al. (eds) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (Cambridge Univ. Press, 2012).

  • Alexander, L. V. et al. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. Atmos. 111, https://doi.org/10.1029/2005JD006290 (2006).

  • Dunn, R. J. H. et al. Development of an updated global land in situ-based data set of temperature and precipitation extremes: HadEX3. J. Geophys. Res. Atmos. 125, https://doi.org/10.1029/2019JD032263 (2020).

  • Roca, R. et al. FROGS: A daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products. Earth Syst. Sci. Data 11, 1017–1035 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Climpact https://climpact-sci.org/ (2012).

  • Python Language Reference, v.3.7 https://www.python.org (2019).

  • Huffman, G. J. et al. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8, 38–55 (2007).

    Article 
    ADS 

    Google Scholar
     

  • Xie, P. et al. Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998. J. Hydrometeorol. 18, 1617–1641 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Bador, M. et al. Impact of higher spatial atmospheric resolution on precipitation extremes over land in global climate models. J. Geophys. Res. Atmos. 125, https://doi.org/10.1029/2019JD032184 (2020).

  • Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article 
    ADS 

    Google Scholar
     

  • Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article 
    ADS 

    Google Scholar
     

  • Sillmann, J. ETCCDI extremes indices archive. https://climate-modelling.canada.ca/climatemodeldata/climdex/index.shtml.

  • Donat, M. G., Angélil, O. & Ukkola, A. M. Intensification of precipitation extremes in the world’s humid and water-limited regions. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ab1c8e (2019).

  • Schulzweida, U. CDO User Guide (2.1.0). Zenodo https://doi.org/10.5281/zenodo.7112925 (2022).

  • Collins, M. et al. Quantifying future climate change. Nat. Clim. Change 2, 403–409 (2012).

    Article 
    ADS 

    Google Scholar
     

  • Eyring, V. et al. Taking climate model evaluation to the next level. Nat. Clim. Chang. 9, 102–110 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Caldwell, P. M. et al. Statistical significance of climate sensitivity predictors obtained by data mining. Geophys. Res. Lett. 41, 1803–1808 (2014).

    Article 
    ADS 

    Google Scholar
     

  • CMIP6 data from WCRP. Google Cloud Catalogue. https://cloud.google.com/datasets.

  • Pangeo Team. PANGEO: A community platform for Big Data geoscience. https://pangeo.io/ (2018).

  • Petrova, I. Y. Observation-constrained projections reveal longer-than-expected dry spells. Source data. Zenodo https://doi.org/10.5281/zenodo.11636527 (2024).

  • Brient, F. Reducing uncertainties in climate projections with emergent constraints: concepts. Source code: emergent constraints. Zenodo https://doi.org/10.5281/zenodo.10886174 (2024).

  • Petrova, I. Y. Observation-constrained projections reveal longer-than-expected dry spells. Source code. Zenodo https://doi.org/10.5281/zenodo.11637360 (2024).

  • Socioeconomic Data and Applications Center. Gridded Population of the World (GPW), v4. https://sedac.ciesin.columbia.edu/data/collection/gpw-v4 (1995).

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