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Recent European drought extremes beyond Common Era background variability


Europe’s recent summer droughts have had devastating ecological and economic consequences, but the severity and cause of these extremes remain unclear. Here we present 27,080 annually resolved and absolutely dated measurements of tree-ring stable carbon and oxygen (δ13C and δ18O) isotopes from 21 living and 126 relict oaks (Quercus spp.) used to reconstruct central European summer hydroclimate from 75 bce to 2018 ce. We find that the combined inverse δ13C and δ18O values correlate with the June–August Palmer Drought Severity Index from 1901–2018 at 0.73 (P < 0.001). Pluvials around 200, 720 and 1100 ce, and droughts around 40, 590, 950 and 1510 ce and in the twenty-first century, are superimposed on a multi-millennial drying trend. Our reconstruction demonstrates that the sequence of recent European summer droughts since 2015 ce is unprecedented in the past 2,110 years. This hydroclimatic anomaly is probably caused by anthropogenic warming and associated changes in the position of the summer jet stream.

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Fig. 1: Growth characteristics and temporal coverage of the central European oak stable isotope dataset.
Fig. 2: Temporal changes in the relation between oak stable isotopes and central European drought.
Fig. 3: Temporal and spatial agreement between the oak stable isotopes and European summer drought.
Fig. 4: Reconstructed central European summer variability over the past 2,110 years.

Data availability statement

The raw tree-ring stable isotope measurements (Supplementary Data 1) and the final drought reconstruction (Supplementary Data 2) are freely available from the NOAA National Centers for Environmental Information (NCEI) at


  1. 1.

    Schär, C. et al. The role of increasing temperature variability in European summer heatwaves. Science 427, 332–336 (2004).

    Google Scholar 

  2. 2.

    Ionita, M. et al. The European 2015 drought from a climatological perspective. Hydrol. Earth Syst. Sci. 21, 1397–1419 (2017).

    Google Scholar 

  3. 3.

    Kornhuber, K. et al. Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern. Environ. Res. Lett. 14, 054002 (2019).

    Google Scholar 

  4. 4.

    Fink, A. H. et al. The 2003 European summer heatwaves and drought – synoptic diagnosis and impacts. Weather 59, 209–216 (2004).

    Google Scholar 

  5. 5.

    Laaha, G. et al. The European 2015 drought from a hydrological perspective. Hydrol. Earth Syst. Sci. 21, 3001–3024 (2017).

    Google Scholar 

  6. 6.

    Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).

    Google Scholar 

  7. 7.

    Van Lanen, H. A. J. et al. Hydrology needed to manage droughts: the 2015 European case. Hydrol. Process. 30, 3097–3104 (2016).

    Google Scholar 

  8. 8.

    Robine, J. M. et al. Death toll exceeded 70,000 in Europe during the summer of 2003. C. R. Biol. 331, 171–178 (2008).

    Google Scholar 

  9. 9.

    García-Herrera, R., Díaz, J., Trigo, R. M., Luterbacher, J. & Fischer, E. M. A review of the European summer heat wave of 2003. Crit. Rev. Environ. Sci. Technol. 40, 267–306 (2010).

    Google Scholar 

  10. 10.

    Haines, A., Koats, R. S., Campbell-Lendrum, D. & Corvalan, C. Climate change and human health: impacts, vulnerability, and mitigation. Lancet 367, 2101–2109 (2006).

    Google Scholar 

  11. 11.

    Amengual, A. et al. Projections of heat waves with high impact on human health in Europe. Glob. Planet. Change 119, 71–84 (2014).

    Google Scholar 

  12. 12.

    Cook, E. R. et al. Old World megadroughts and pluvials during the Common Era. Sci. Adv. 1, e150056 (2015).

    Google Scholar 

  13. 13.

    Ljungqvist, F. C. et al. Northern Hemisphere hydroclimate variability over the past twelve centuries. Nature 532, 94–98 (2016).

    Google Scholar 

  14. 14.

    Gomez-Navarro, J. J., Montavez, J. P., Wagner, S. & Zorita, E. A regional climate palaeosimulation for Europe in the period 1500–1990. Part 1: model validation. Clim. Past 9, 1667–1682 (2013).

    Google Scholar 

  15. 15.

    Gomez-Navarro, J. J. et al. A regional climate palaeosimulation for Europe in the period 1500–1990. Part 2: shortcomings and strengths of models and reconstructions. Clim. Past 11, 1077–1095 (2015).

    Google Scholar 

  16. 16.

    Frankcombe, L. M., England, M. H., Mann, M. E. & Steinman, B. A. Separating internal variability from the externally forced climate response. J. Clim. 28, 8184–8202 (2015).

    Google Scholar 

  17. 17.

    Ryu, J. H. & Hayhoe, K. Observed and CMIP5 modeled influence of large-scale circulation on summer precipitation and drought in the South-Central United States. Clim. Dynam. 49, 4293–4310 (2017).

    Google Scholar 

  18. 18.

    Fischer, E. M. & Schär, C. Future changes in daily summer temperature variability: driving processes and role for temperature extremes. Clim. Dynam. 33, 917–935 (2009).

    Google Scholar 

  19. 19.

    Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth-Sci. Rev. 99, 125–161 (2010).

    Google Scholar 

  20. 20.

    Barella-Ortiz, A. & Quintana-Seguí, P. Evaluation of drought representation and propagation in regional climate model simulations across Spain. Hydrol. Earth Syst. Sci. 23, 5111–5131 (2019).

    Google Scholar 

  21. 21.

    Ljungqvist, F. C. et al. Warm-season temperature and hydroclimate co-variability across Europe since 850 CE. Environ. Res. Lett. 14, 084015 (2019).

    Google Scholar 

  22. 22.

    Büntgen, U. et al. No age trends in oak stable isotopes. Paleocean. Paleoclim. 34, e2019PA003831 (2020).

  23. 23.

    Urban, O. et al. The dendroclimatic value of oak stable isotopes. Dendrochronologia 65, 125804 (2021).

    Google Scholar 

  24. 24.

    Tegel, W., Vanmoerkerke, J. & Büntgen, U. Updating historical tree-ring records for climate reconstruction. Quat. Sci. Rev. 29, 1957–1959 (2010).

    Google Scholar 

  25. 25.

    Belmecheri, S. & Lavergne, A. Compiled records of atmospheric CO2 concentrations and stable carbon isotopes to reconstruct climate and derive plant ecophysiological indices from tree rings. Dendrochronologia 63, 125748 (2020).

    Google Scholar 

  26. 26.

    van der Schrier, G., Briffa, K. R., Jones, P. D. & Osborn, T. J. Summer moisture availability across Europe. J. Clim. 19, 2819–2834 (2006).

    Google Scholar 

  27. 27.

    Boettger, T., Haupt, M., Friedrich, M. & Waterhouse, J. S. Reduced climate sensitivity of carbon, oxygen and hydrogen stable isotope ratios in tree-ring cellulose of silver fir (Abies alba Mill.) influenced by background SO2 in Franconia (Germany, Central Europe). Environ. Pollut. 185, 281–294 (2014).

    Google Scholar 

  28. 28.

    Bunde, A., Büntgen, U., Ludescher, J., Luterbacher, J. & von Storch, H. Is there memory in precipitation? Nat. Clim. Change 3, 174–175 (2013).

    Google Scholar 

  29. 29.

    Saurer, M., Aellen, K. & Siegwolf, R. Correlating δ13C and δ18O in cellulose of trees. Plant Cell Environ. 20, 1543–1550 (1997).

    Google Scholar 

  30. 30.

    Masson-Delmotte, V. et al. Changes in European precipitation seasonality and in drought frequencies revealed by a four-century-long tree-ring isotopic record from Brittany, western France. Clim. Dynam. 24, 57–69 (2005).

    Google Scholar 

  31. 31.

    Farquhar, G. D., Ehleringer, J. R. & Hubick, K. T. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 40, 503–537 (1989).

    Google Scholar 

  32. 32.

    Roden, J. S., Lin, G. & Ehleringer, J. R. A mechanistic model for interpretation of hydrogen and oxygen isotope ratios in tree-ring cellulose. Geochim. Cosmochim. Acta 64, 21–35 (2000).

    Google Scholar 

  33. 33.

    Scheidegger, Y., Saurer, M., Bahn, M. & Siegwolf, R. Linking stable oxygen and carbon isotopes with stomatal conductance and photosynthetic capacity: a conceptual model. Oecologia 125, 350–357 (2000).

    Google Scholar 

  34. 34.

    Treydte, K. et al. Signal strength and climate calibration of a European tree-ring isotope network. Geophys. Res. Lett. 34, L24302 (2007).

    Google Scholar 

  35. 35.

    Young, G. H. F. et al. Oxygen stable isotope ratios from British oak tree‑rings provide a strong and consistent record of past changes in summer rainfall. Clim. Dynam. 45, 3609–3622 (2015).

    Google Scholar 

  36. 36.

    Labuhn, I. et al. French summer droughts since 1326 CE: a reconstruction based on tree ring cellulose δ18O. Clim. Past 12, 1101–1117 (2016).

    Google Scholar 

  37. 37.

    Trnka, M. et al. Mitigation efforts will not fully alleviate the increase in water scarcity occurrence probability in wheat-producing areas. Sci. Adv. 5, eaau2406 (2019).

    Google Scholar 

  38. 38.

    Büntgen, U. et al. Cooling and societal change during the Late Antique Little Ice Age from 536 to around 660 AD. Nat. Geosci. 9, 231–236 (2016).

    Google Scholar 

  39. 39.

    Büntgen, U. & Di Cosmo, N. Climatic and environmental aspects of the Mongol withdrawal from Hungary in 1242 CE. Sci. Rep. 6, 25606 (2016).

    Google Scholar 

  40. 40.

    Schmid, B. V. et al. Climate-driven introduction of the Black Death and successive plague reintroductions into Europe. Proc. Natl Acad. Sci. USA 112, 3020–3025 (2015).

    Google Scholar 

  41. 41.

    Brázdil, R. et al. Documentary and instrumental-based drought indices for the Czech Lands back to AD 1501. Clim. Res. 70, 103–117 (2016).

    Google Scholar 

  42. 42.

    Mozny, M. et al. Drought reconstruction based on grape harvest dates for the Czech Lands. 1499–2012. Clim. Res. 70, 119–132 (2016).

    Google Scholar 

  43. 43.

    Büntgen, U. et al. 2500 years of European climate variability and human susceptibility. Science 331, 578–582 (2011).

    Google Scholar 

  44. 44.

    Dobrovolny, P. et al. Monthly, seasonal and annual temperature reconstructions for Central Europe derived from documentary evidence and instrumental records since AD 1500. Clim. Change 101, 69–107 (2010).

    Google Scholar 

  45. 45.

    Büntgen, U., Frank, D. C., Nievergelt, D. & Esper, J. Summer temperature variations in the European Alps, AD 755-2004. J. Clim. 19, 5606–5623 (2006).

    Google Scholar 

  46. 46.

    Luterbacher, J. et al. European summer temperatures since Roman times. Environ. Res. Lett. 11, 024001 (2016).

    Google Scholar 

  47. 47.

    Esper, J. et al. Orbital forcing of tree-ring data. Nat. Clim. Change 2, 862–866 (2012).

    Google Scholar 

  48. 48.

    Wang, Y. et al. The Holocene Asian monsoon: links to solar changes and North Atlantic climate. Science 308, 854–857 (2005).

    Google Scholar 

  49. 49.

    Chen, F. et al. East Asian summer monsoon precipitation variability since the last deglaciation. Sci. Rep. 5, 11186 (2015).

    Google Scholar 

  50. 50.

    Cheng, H. et al. The Asian monsoon over the past 640,000 years and ice age terminations. Nature 534, 640–646 (2016).

    Google Scholar 

  51. 51.

    Routson, C. C. et al. Mid-latitude net precipitation decreased with Arctic warming during the Holocene. Nature 568, 83–87 (2019).

    Google Scholar 

  52. 52.

    Büntgen, U. et al. New tree-ring evidence from the Pyrenees reveals western Mediterranean climate variability since medieval times. J. Clim. 30, 5295–5318 (2017).

    Google Scholar 

  53. 53.

    Rao, M. P. et al. European and Mediterranean hydroclimate responses to tropical volcanic forcing over the last millennium. Geophys. Res. Lett. 44, 5104–5112 (2017).

    Google Scholar 

  54. 54.

    Toohey, M. & Sigl, M. Volcanic stratospheric sulfur injections and aerosol optical depth from 500 BCE to 1900 CE. Earth Syst. Sci. Data 9, 809–831 (2017).

    Google Scholar 

  55. 55.

    García‐Herrera, R. et al. The European 2016/2017 drought. J. Clim. 32, 3169–3187 (2019).

    Google Scholar 

  56. 56.

    Braconnot, P. et al. Evaluation of climate models using palaeoclimatic data. Nat. Clim. Change 2, 417–424 (2012).

    Google Scholar 

  57. 57.

    Dobrovolný, P., Brázdil, R., Trnka, M., Kotyza, O. & Valášeket, H. Precipitation reconstruction for the Czech Lands, AD 1501–2010. Int. J. Climatol. 35, 1–14 (2015).

    Google Scholar 

  58. 58.

    Büntgen, U. et al. Tree-ring indicators of German summer drought over the last millennium. Quat. Sci. Rev. 29, 1005–1016 (2010).

    Google Scholar 

  59. 59.

    Dobrovolny, P. et al. May–July precipitation reconstruction from oak tree-rings for Bohemia (Czech Republic) since AD 1040. Int. J. Climatol. 38, 1910–1924 (2017).

    Google Scholar 

  60. 60.

    Rybníček, M. et al. Oak (Quercus spp.) response to climate differs more among sites than among species incentral Czech Republic. Dendrobiology 75, 55–65 (2016).

    Google Scholar 

  61. 61.

    Knibbe, B. Personal Analysis System for Tree-ring Research 4—Instruction Manual (SCIEM, 2004).

  62. 62.

    Gissino-Mayer, H. D. Evaluating crossdating accuracy: a manual and tutorial for the computer program COFECHA. Tree Ring Res. 57, 205–221 (2001).

    Google Scholar 

  63. 63.

    Prokop, O. et al. On the paleoclimatic potential of a millennium-long oak ring width chronology from Slovakia. Dendrochronologia 40, 93–101 (2016).

    Google Scholar 

  64. 64.

    Boettger, T. et al. Wood cellulose preparation methods and mass spectrometric analyses of δ13C, δ18O, and nonexchangeable δ2H values in cellulose, sugar, and starch: an interlaboratory comparison. Anal. Chem. 79, 4603–4612 (2007).

    Google Scholar 

  65. 65.

    Frank, D., Esper, J. & Cook, E. R. Adjustment for proxy number and coherence in a large-scale temperature reconstruction. Geophys. Res. Lett. 34, L16709 (2007).

    Google Scholar 

  66. 66.

    Briffa, K. R. et al. Fennoscandian summers from AD 500: temperature changes on short and long timescales. Clim. Dynam. 7, 111–119 (1992).

    Google Scholar 

  67. 67.

    Esper, J., Cook, E. R., Krusic, P. J., Peters, K. & Schweingruber, F. H. Tests of the RCS method for preserving low-frequency variability in long tree-ring chronologies. Tree Ring Res. 59, 81–98 (2003).

    Google Scholar 

  68. 68.

    Melvin, T. M. & Briffa, K. R. A “signal-free” approach to dendroclimatic standardisation. Dendrochronologia 26, 71–86 (2008).

    Google Scholar 

  69. 69.

    Cook, E. R. & Krusic, P. J. RCSsigFree_v45h: a signal-free chronology development and analysis tool for dendrochronology (Lamont-Doherty Earth Observatory, 2007);

  70. 70.

    Melvin, T. M., Briffa, K. R., Nicolussi, K. & Grabner, M. Time-varying-response smoothing. Dendrochronologia 25, 65–69 (2007).

    Google Scholar 

  71. 71.

    Osborn, T. J., Briffa, K. R. & Jones, P. D. Adjusting variance for sample size in tree-ring chronologies and other regional mean timeseries. Dendrochronologia 15, 89–99 (1997).

    Google Scholar 

  72. 72.

    Cook, E. R., Meko, D. M., Stahle, D. W. & Cleaveland, M. K. Drought reconstructions for the continental United States. J. Clim. 12, 1145–1162 (1999).

    Google Scholar 

  73. 73.

    Coplen, T. B. Discontinuance of SMOW and PDB. Nature 375, 285 (1995).

    Google Scholar 

  74. 74.

    Hamed, K. H. & Rao, A. R. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol. 204, 182–196 (1998).

    Google Scholar 

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The work was supported by the Czech Republic Grant Agency (grant numbers 17-22102S and 18-11004S), and the SustES project—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797). U.B. and J.E. received funding from the ERC project Monostar (grant number AdG 882727) and W.T. acknowledges the German Research Foundation (grant number TE 613/3-2). Relict oak samples from Bavaria were collected and cross-dated by F. Herzig, and I. Roshka and N. Pernicová contributed to sample preparation.

Author information




U.B. and M.T. designed the study. U.B. and P.J.K. performed the analyses and wrote the manuscript. M.R., T. Kolář, T. Kyncl and E.K. developed the Czech oak tree-ring dataset and prepared samples for isotopic analyses. O.U., A.A. and J.Č. processed and measured the stable isotopes. P.J.K. helped to develop the hydroclimatic reconstruction and S.W. provided model data and interpretation. J.E., M.S., W.T., P.D., P.C., F.R. and M.T. helped to place the results in a wider physiological, climatological and historical context. All authors provided critical discussion, helped to write and revise the manuscript and approved its submission.

Corresponding author

Correspondence to Ulf Büntgen.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature Geoscience thanks Cody Routson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super.

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

Extended data

Extended Data Fig. 1 Oak network.

Spatial distribution of 147 living, historical, archaeological and subfossil oaks between 91 BCE and 2018 CE, for which TRW, δ18O and δ13C were measured at annual resolution. While the vast majority of samples originates from the Czech Republic, a few archaeological samples come from Bavaria in south-eastern Germany.

Extended Data Fig. 2 Dendro inventory.

Number of individual tree-ring samples (series), the total chronology length and its start and end year (year, start, end), the mean series length (msl), the minimum, mean and maximum raw measurement values (min, mean, max), as well as the standard deviation, mean sensitivity and first-order autocorrelation coefficient (stdev, sens, ac1), of the four dendro parameters: δ13C, δ13C corrected, δ18O and tree-ring width (13C, 13Ccorr, 18O, TRW). The compound TRSI data are z-scores (mean of zero and standard deviation of one). Carbon and oxygen isotope ratios are reported in per mil (‰) using the usual delta (δ) notation relative to the VPDB (δ13C) and VSMOW (δ18O) standards73.

Extended Data Fig. 3 Temperature sensitivity.

Pearson’s correlation coefficients between the non-standardized δ18O (blue dots) and δ13C (red dots) records (using the median of the individual measurements), as well as their simple average (green circles), and monthly (from previous year January to current year December) and seasonal (all possible 28 monthly pairings between March and October of the growing season: Mar-Apr, Mar-May, Mar-Jun, Mar-Jul, Mar-Aug, Mar-Sep, Mar-Oct, Apr-May, Apr-Jun, Apr-Jul, Apr-Aug, Apr-Sep, Apr-Oct, May-Jun, May-Jul, May-Aug, May-Sep, May-Oct, Jun-Jul, Jun-Aug, Jun-Sep, Jun-Oct, Jul-Aug, Jul-Sep, Jul-Oct, Aug-Sep, Aug-Oct, Sep-Oct) temperature averages over 49–50°N and 15–18°E. Correlations are calculated over the early, late and full period of proxy-target overlap (from left to right).

Extended Data Fig. 4 Isotopic behaviour.

(a) Comparison of the non-standardized, inverse δ18O record (blue) against the non-standardized, inverse and corrected δ13C records (red) using the median of the individual measurements. (b) Difference between the annual δ18O and δ13C values, with the straight line referring to their long-term trend (equation in brackets). (c) Simple average and long-term trend of the annual δ18O and δ13C data. All timeseries cover the period 75 BCE to 2018 CE, during which at least ten samples are included each year. The smoothed curves in (a) and (c) are 50-year low-pass filters.

Extended Data Fig. 5 Calibration-verification statistics.

Statistical information of the full (1901–2018) calibration model, as well as using two equally-long early/late (1901–1959 and 1960–2018) split period calibration windows, for which the corresponding verification results are provided as well. Each column represents a different measure of interaction between the climate target and proxy variable along with, where appropriate, the probability (Pct) of obtaining that value by chance alone, the exceptions being RE (Reduction of Error), and CE (Coefficient of Efficiency). The four measures are, the Pearson, Robust Pearson, and Spearman correlations, and the statistical significance of the Cross Product (Xprod) between X and Y (Corr = correlation, Med = Median, tstat = t-statistic).

Extended Data Fig. 6 Reconstructed hydroclimatic extremes.

The 20 highest (that is, wettest) and lowest (that is, driest) annual JJA scPDSI values between 75 BCE and 2018 CE (including year zero). The two wettest and driest 4-year and 5-year periods of consecutive JJA scPDSI values (for example, 2018 refers to 2015–2018 and 2014–2018 for the four- and five-year periods, respectively).

Extended Data Fig. 7 Reconstruction uncertainty.

(a) Temporal evolution of the reconstruction’s annual error range that combines measurement (Standard Error) and calibration (Root Mean Squared Error) uncertainties. Note that the error range is consistently decreasing towards present, that is, uncertainty was generally lager in the first half of the Common Era (y = −0.0001x + 3.6204, R² = 0.0551). (b) Expressed Population Signal (EPS) of the combined δ18O and δ13C dataset (compound TRSI), and calculated over 50-year windows, lagged by 25 years. (c) Sample size of all TRSI ranges between 10 and 42 series per year.

Extended Data Fig. 8 Trend behaviour.

(a) Linear regression fitted to the JJA scPDSI reconstruction from 75 BCE to 2018 CE (with 2094 degrees of freedom). The Root Mean Squared Error (RMSE) is 1.93, the R-squared value is 0.112, the adjusted R-Squared is 0.112, and the F-statistic versus constant model is 266 (p-value = 1.78e-56). (b) Liner trends of the full the JJA scPDSI reconstruction and three pre-industrial periods (orange), as well as three industrial periods (red). Results from the Mann-Kendall test74, modified to account for autocorrelation on the timeseries, reveal there is a significant (p < 0.01) negative trend in the reconstructed values.

Extended Data Fig. 9 Common Era climate history.

(a) This study compared against (b) central European JJA scPDSI from the OWDA (ref. 12) centred over 49.5°N and 16.5°E, and (c) European JJA temperature anomalies46. Thick curves are 50-year cubic smoothing splines and dashed lines long-term trends.

Extended Data Fig. 10 Volcanic forcing.

(a) Reconstructed JJA scPDSI during five periods of strong volcanism. (b) Superposed composites of the JJA scPDSI reconstruction aligned over the 12 (17) strongest individual volcanic forcing events before (after) 1200 CE, as well as using 12 known Icelandic eruptions between 1200 and 1900 CE and a subset of 24 of the strongest non-Icelandic eruptions54. Peak volcanic forcing either appears in year zero or year one following the volcanic eruption depending on the latitude and season. Forcing and response are calculated relative to a pre-event 5-year background period presumably undisturbed by volcanic forcing (for example, 1804–1808 for the 1809 and 1815 volcanic eruptions, respectively). Data after secondary eruptions (for example, data from lag +6 years following the 1809 eruption) are removed prior to data aggregation.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5.

Supplementary Data 1

TRSI data. Includes annually resolved oxygen and carbon isotope composition records.

Supplementary Data 2

Data and summary plot of the reconstructed (recon) summer (JJA) scPDSI. The data are also fitted with a 50-yr average spline and accompanied by the final error ranges. These data are also plotted in Fig. 4.

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Büntgen, U., Urban, O., Krusic, P.J. et al. Recent European drought extremes beyond Common Era background variability. Nat. Geosci. 14, 190–196 (2021).

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