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Empirical estimate of forestation-induced precipitation changes in Europe


Land-cover changes can affect the climate by altering the water and energy balance of the land surface. Numerous modelling studies have indicated that alterations at the land surface can result in considerable changes in precipitation. Yet land-cover-induced precipitation changes remain largely unconstrained by observations. Here we use an observation-based continental-scale statistical model to show that forestation of rain-fed agricultural land in Europe triggers substantial changes in precipitation. Locally, we find an increase in precipitation following forestation, in particular in winter, which is supported by a paired rain-gauge analysis. In addition, forests are estimated to increase downwind precipitation in most regions during summer. By contrast, the downwind effect in winter is positive in coastal areas but near neutral and negative in Continental and Northern Europe, respectively. The combined local and non-local effects of a realistic reforestation scenario, constrained by sustainability safeguards, are estimated to increase summer precipitation by 7.6 ± 6.7% on average over Europe (0.13 ± 0.11 mm d–1), potentially offsetting a substantial part of the projected precipitation decrease from climate change. We therefore conclude that land-cover-induced alterations of precipitation should be considered when developing land-management strategies for climate change adaptation and mitigation.

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Fig. 1: Local precipitation difference between forest and agricultural land at site pairs.
Fig. 2: Theoretical precipitation change from foresting 20% of the land surface across Europe.
Fig. 3: Potential precipitation change from realistic forestation.
Fig. 4: Comparison of precipitation changes from climate change and forestation.

Data availability

MSWEP v2.2 can be retrieved from EU-DEM v1.1 can be downloaded from, CORINE LC 2000 from, the Global Map of Irrigation Areas version 5 from and the ERA5(-Land) reanalysis data from The GSDR station data are available upon request from E.L. ( but require permission to use the respective data from several institutions23. GHCN_Daily v3.x can be retrieved from The World Reforestation Potential map can be downloaded from To access the CH2018 collection, follow the instructions at, where there is also a contact form to access specialized data. The base maps of all figures displaying Europe were generated with the standard ‘landareas’ file of Matlab, which is based on the Digital Chart of the World69. The source data files are available at the ETH Research Collection under

Code availability

The R library mgvc can be found at and the GDAL library at


  1. Lee, X. et al. Observed increase in local cooling effect of deforestation at higher latitude. Nature 479, 384–387 (2011).

    Article  Google Scholar 

  2. Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. (2015).

  3. Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. (2018).

  4. Jia, G. et al. in Special Report on Climate Change and Land (eds Shukla, P. R. et al.) Ch. 2 (IPCC, 2019).

  5. Lejeune, Q., Seneviratne, S. I. & Davin, E. L. Historical land-cover change impacts on climate: comparative assessment of LUCID and CMIP5 multimodel experiments. J. Clim. 30, 1439–1459 (2017).

    Article  Google Scholar 

  6. Winckler, J., Reick, C. H. & Pongratz, J. Robust identification of local biogeophysical effects of land-cover change in a global climate model. J. Clim. 30, 1159–1176 (2017).

    Article  Google Scholar 

  7. Duveiller, G. et al. Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations. Earth Syst. Sci. Data 10, 1265–1279 (2018).

    Article  Google Scholar 

  8. Meier, R. et al. Evaluating and improving the Community Land Model’s sensitivity to land cover. Biogeosciences 15, 4731–4757 (2018).

    Article  Google Scholar 

  9. Meier, R., Davin, E. L., Swenson, S. C., Lawrence, D. M. & Schwaab, J. Biomass heat storage dampens diurnal temperature variations in forests. Environ. Res. Lett. 14, 084026 (2019).

    Article  Google Scholar 

  10. Spracklen, D., Arnold, S. & Taylor, C. Observations of increased tropical rainfall preceded by air passage over forests. Nature 489, 282–285 (2012).

    Article  Google Scholar 

  11. Lejeune, Q., Davin, E. L., Guillod, B. P. & Seneviratne, S. I. Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation. Clim. Dyn. 44, 2769–2786 (2015).

    Article  Google Scholar 

  12. Khanna, J., Medvigy, D., Fueglistaler, S. & Walko, R. Regional dry-season climate changes due to three decades of Amazonian deforestation. Nat. Clim. Change 7, 200–204 (2017).

    Article  Google Scholar 

  13. Yosef, G. et al. Large-scale semi-arid afforestation can enhance precipitation and carbon sequestration potential. Sci. Rep. (2018).

  14. Belušić, D., Fuentes-Franco, R., Strandberg, G. & Jukimenko, A. Afforestation reduces cyclone intensity and precipitation extremes over Europe. Environ. Res. Lett. 14, 074009 (2019).

    Article  Google Scholar 

  15. Perugini, L. et al. Biophysical effects on temperature and precipitation due to land cover change. Environ. Res. Lett. 12, 053002 (2017).

    Article  Google Scholar 

  16. Sandel, B. & Svenning, J. Human impacts drive a global topographic signature in tree cover. Nat. Commun. (2013).

  17. Fuchs, R., Herold, M., Verburg, P. H. & Clevers, J. G. P. W. A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe. Biogeosciences 10, 1543–1559 (2013).

    Article  Google Scholar 

  18. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article  Google Scholar 

  19. Fuchs, R., Herold, M., Verburg, P. H., Clevers, J. G. & Eberle, J. Gross changes in reconstructions of historic land cover/use for Europe between 1900 and 2010. Glob. Change Biol. 21, 299–313 (2014).

    Article  Google Scholar 

  20. McGrath, M. J. et al. Reconstructing European forest management from 1600 to 2010. Biogeosciences 12, 4291–4316 (2015).

    Article  Google Scholar 

  21. Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).

    Article  Google Scholar 

  22. Navarro, L. M. & Pereira, H. M. Rewilding Abandoned Landscapes in Europe (Springer, 2015).

  23. Lewis, E. et al. GSDR: a global sub-daily rainfall dataset. J. Clim. 32, 4715–4729 (2019).

    Article  Google Scholar 

  24. Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. & Houston, T. G. An overview of the global historical climatology network-daily database. J. Atmos. Ocean. Technol. 29, 897–910 (2012).

    Article  Google Scholar 

  25. Menne, M. J. et al. Global Historical Climatology Network—Daily (GHCN-Daily) Version 3.20 (NOAA, 2012);

  26. Zhang, M. et al. Response of surface air temperature to small-scale land clearing across latitudes. Environ. Res. Lett. (2014).

  27. Liu, H., Randerson, J. T., Lindfors, J. & Chapin, F. S. III Changes in the surface energy budget after fire in boreal ecosystems of interior Alaska: an annual perspective. J. Geophys. Res. (2005).

  28. Juang, J.-Y., Katul, G., Siqueira, M., Stoy, P. & Novick, K. Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophys. Res. Lett. (2007).

  29. Vanden Broucke, S., Luyssaert, S., Davin, E. L., Janssens, I. & van Lipzig, N. New insights in the capability of climate models to simulate the impact of LUC based on temperature decomposition of paired site observations. J. Geophys. Res. Atmos. 120, 5417–5436 (2015).

    Article  Google Scholar 

  30. Beck, H. E. et al. MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment. Bull. Am. Meteorol. Soc. 100, 473–500 (2019).

    Article  Google Scholar 

  31. Schwaab, J. et al. Increasing the broad-leaved tree fraction in European forests mitigates hot temperature extremes. Sci. Rep. 10, 14153 (2020).

    Article  Google Scholar 

  32. Cohn, A. S. et al. Forest loss in Brazil increases maximum temperatures within 50 km. Environ. Res. Lett. 14, 084047 (2019).

    Article  Google Scholar 

  33. Houze, R. A. Jr Orographic effects on precipitating clouds. Rev. Geophys. (2012).

  34. C3S ERA5-Land Reanalysis (Copernicus Climate Change Service, 2019).

  35. Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064 (2008).

    Article  Google Scholar 

  36. Sprenger, M. & Wernli, H. The LAGRANTO Lagrangian analysis tool—version 2.0. Geosci. Model Dev. 8, 2569–2586 (2015).

    Article  Google Scholar 

  37. Kosztra, B., Büttner, G., Hazeu, G. & Arnold, S. Updated CLC Illustrated Nomenclature Guidelines (European Environment Agency, 2019).

  38. Duveiller, G., Fasbender, D. & Meroni, M. Revisiting the concept of a symmetric index of agreement for continuous datasets. Sci. Rep. 6, 19401 (2016).

    Article  Google Scholar 

  39. Griscom, B. W. et al. Global Reforestation Potential Map (Zenodo, 2017);

  40. Sheffield, J. & Wood, E. F. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Clim. Dyn. 31, 79–105 (2008).

    Article  Google Scholar 

  41. Kotlarski, S. et al. Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci. Model Dev. 7, 1297–1333 (2014).

    Article  Google Scholar 

  42. Prein, A. F. et al. A review on regional convection-permitting climate modeling: demonstrations, prospects, and challenges. Rev. Geophys. 53, 323–361 (2015).

    Article  Google Scholar 

  43. Liu, J. & Niyogi, D. Meta-analysis of urbanization impact on rainfall modification. Sci. Rep. (2019).

  44. Van der Ent, R. J. & Savenije, H. H. G. Length and time scales of atmospheric moisture recycling. Atmos. Chem. Phys. 11, 1853–1863 (2011).

    Article  Google Scholar 

  45. Rüdisühli, S., Sprenger, M., Leutwyler, D., Schär, C. & Wernli, H. Attribution of precipitation to cyclones and fronts over Europe in a kilometer-scale regional climate simulation. Weather Clim. Dyn. 1, 675–699 (2020).

    Article  Google Scholar 

  46. Schultz, N. M., Lawrence, P. J. & Lee, X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. J. Geophys. Res. Biogeosci. 122, 903–917 (2017).

    Article  Google Scholar 

  47. Pollock, M. D. et al. Quantifying and mitigating wind-induced undercatch in rainfall measurements. Water Resour. Res. 54, 3863–3875 (2018).

    Article  Google Scholar 

  48. Trabucco, A., Zomer, R. J., Bossio, D. A., Straaten], O. V. & Verchot, L. V. Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agr. Ecosyst. Environ. 126, 81–97 (2008).

    Article  Google Scholar 

  49. Padrón, R. S., Gudmundsson, L., Greve, P. & Seneviratne, S. I. Large-scale controls of the surface water balance over land: insights from a systematic review and meta-analysis. Water Resour. Res. 53, 9659–9678 (2017).

    Article  Google Scholar 

  50. Beck, H. E. et al. MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci. 21, 589–615 (2017).

    Article  Google Scholar 

  51. Beck, H. E. et al. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol. Earth Syst. Sci. 21, 6201–6217 (2017).

    Article  Google Scholar 

  52. Beck, H. E. et al. Daily evaluation of 26 precipitation datasets using stage-IV gauge-radar data for the CONUS. Hydrol. Earth Syst. Sci. 23, 207–224 (2019).

    Article  Google Scholar 

  53. Lu, N. Scale effects of topographic ruggedness on precipitation over Qinghai-Tibet Plateau. Atmos. Sci. Lett. 20, e904 (2019).

    Article  Google Scholar 

  54. EU-DEM Statistical Validation (EEA, 2014).

  55. Siebert, S., Henrich, V., Frenken, K. & Burke, J. Global Map of Irrigation Areas Version 5 (Rheinische Friedrich-Wilhelms-University and FAO, 2013).

  56. DeAngelis, A. et al. Evidence of enhanced precipitation due to irrigation over the Great Plains of the United States. J. Geophys. Res. Atmos. (2010).

  57. Thiery, W. et al. Present-day irrigation mitigates heat extremes. J. Geophys. Res. 122, 1403–1422 (2017).

    Article  Google Scholar 

  58. Wernli, B. H. & Davies, H. C. A Lagrangian-based analysis of extratropical cyclones. I: the method and some applications. Q. J. R. Meteorol. Soc. 123, 467–489 (1997).

    Article  Google Scholar 

  59. Smith, A., Lott, N. & Vose, R. The integrated surface database: recent developments and partnerships. Bull. Am. Meteorol. Soc. 92, 704–708 (2011).

    Article  Google Scholar 

  60. Blenkinsop, S., Lewis, E., Chan, S. C. & Fowler, H. J. Quality-control of an hourly rainfall dataset and climatology of extremes for the UK. Int. J. Climatol. 37, 722–740 (2017).

    Article  Google Scholar 

  61. Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn (CRC Press, 2017).

  62. Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. 73, 3–36 (2011).

    Article  Google Scholar 

  63. Wood, S. N., Li, Z., Shaddick, G. & Augustin, N. H. Generalized additive models for gigadata: modeling the UK black smoke network daily data. J. Am. Stat. Assoc. 112, 1199–1210 (2017).

    Article  Google Scholar 

  64. Li, Z. & Wood, S. N. Faster model matrix crossproducts for large generalized linear models with discretized covariates. Stat. Comput. 30, 19–25 (2020).

    Article  Google Scholar 

  65. Dormann, C. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).

    Article  Google Scholar 

  66. CH2018. 2018 Climate Scenarios for Switzerland (National Centre for Climate Services, 2018).

  67. Prein, A. F. et al. Precipitation in the EURO-CORDEX 0.11° and 0.44° simulations: high resolution, high benefits? Clim. Dyn. 46, 383–412 (2016).

    Article  Google Scholar 

  68. Jacob, D. et al. EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg. Environ. Change 14, 563–578 (2014).

    Article  Google Scholar 

  69. Digital Chart of the World (DMA and USGS, 1992).

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We are thankful for the help and advice of A. Papritz, F. Scholder-Aemisegger, M. Windisch and A. Gilgen. This work is part of the CLIMPULSE project, which was funded by the Swiss National Science Foundations (SNSF;; grant no. 200021_172715) and the Swiss Federal Office for the Environment (FOEN). The GSDR data analysed in this study contain measurements of the following institutes: Service Puplic de Wallonia, Finnish Meteorological Institute, Météo-France, Deutscher Wetterdienst, Met Éireann, Meteo Trentino, Agrometeorologico Siciliano, Autonome Provinz Bozen-Südtirol, Norwegean Meteorological Institute, Sistema Nacional de Informação de Recursos Hídricos, Instituto Português do Mar e da Atmosfera, Servei Meteorologic de Catalunya, Meteo Schweiz, UK Met Office (Met Office (2006): MIDAS UK Hourly Rainfall Data. NCAS British Atmospheric Data Centre, May 5th 2020;, Environment Agency UK, the Scottish Environment Protection Agency (contains public-sector information licenced under the Open Government Licence v3.0), and Natural Resources Wales (contains Natural Resources Wales information © Natural Resources Wales and database right. All rights reserved). The post-processed model data of CORDEX are provided by the Center for Climate Systems Modeling (C2SM), ETH Zurich (J. Rajczak, S. Soerland, U. Beyerle, C. Spirig and E. Zubler).

Author information

Authors and Affiliations



R.M. and E.L.D. conceptualized the study with inputs from J.S. and S.I.S. R.M. and J.S. prepared the gridded data and conducted the GAM analysis. R.M. and M.S. calculated the wind trajectories with the Lagranto tool. R.M. and E.L. prepared and analysed the GSDR station data. R.M. conducted the analysis of the GHCN data. R.M. drafted the manuscript with the help of E.L.D., J.S. and S.I.S. All authors contributed to the interpretation of results and preparation of the text.

Corresponding authors

Correspondence to Ronny Meier or Edouard L. Davin.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Richard Betts, Nathalie de Noblet-Ducoudré and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson.

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

Extended data

Extended Data Table 1 Smooths used to construct the GAM

Extended Data Fig. 1 Overview of the steps, softwares, and input data sets described in the Methods and how they are interconnected.

White boxes, input data sets and yellow boxes softwares used in the study (Supplementary Table 2). The blue boxes show variables that were used as predictor variables in the GAMs and for the selection criteria in the site pair analysis, the green boxes derived data sets and objects, and red boxes the final results.

Extended Data Fig. 2 Examples of trajectory-based fields.

Upwind forest fraction in January (a) and August (b). Panel c shows the upwind fraction of ALr in August. Panel d shows the upwind distance to coast, panel e the upwind height difference, and panel f the downwind height difference in January.

Extended Data Fig. 3 Location of rain gauge stations.

Location of the GSDR (purple) and GHCN (orange) rain gauge stations included in the site pair analysis.

Extended Data Fig. 4 Local precipitation difference between forest and agricultural land at site pairs.

As Fig. 1 d but for Regions 1, 2, 4 and 5.

Extended Data Fig. 5 Relative local precipitation difference between forest and agricultural land at site pairs.

The median (black line), interquartile range (colored shading), and range between 10th and 90th percentile (grey shading) of ΔPloc in the rain gauge data sets as a fraction of the precipitation at the site with more ALr over the five regions in Fig. 1 a.

Extended Data Fig. 6 Changes in LC fractions for uniform forestation scenario.

Panel a, change in local ALr and forest fractions used to predict the local effect of forestation with the GAM (calculated from Eq. (3)). To the right, the change in the upwind forest fraction following 20 % forestation in January (b) and July (c), retrieved from recalculating the upwind LC fractions in the trajectories with the altered LC maps.

Extended Data Fig. 7 Local precipitation difference between forest and agricultural land at site pairs with relaxed selection criteria.

As Fig. 1 but with relaxed selection criteria (ID 10 in Supplement D). Note that Panel d shows Region 6 instead of Region 3.

Supplementary information

Supplementary Information

Supplementary Sections A–E, Figs. 1–10 and Tables 1–5.

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Meier, R., Schwaab, J., Seneviratne, S.I. et al. Empirical estimate of forestation-induced precipitation changes in Europe. Nat. Geosci. 14, 473–478 (2021).

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