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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


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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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