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Nature Geoscience Article https://doi.org/10.1038/s41561-025-01742-z Acknowledgements C.T., Z.B. and P.T. were supported by the NKFIH K 146137, FK 142428 and KKP 144068 grants, respectively. The support of the János Bolyai Research Scholarship of the Hungarian Academy of Sciences and the New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund (ÚNKP-23-5-SZTE-697) are also acknowledged. V.M.T. was supported by the German Ministry for Education and Research (BMBF) and by the state of Lower Saxony. F.A.O.S. was supported by grants from FAPEMIG. Á.B.-F. was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. This work has been implemented by the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014) project within the framework of Hungary’s National Recovery and Resilience Plan supported by the Recovery and Resilience Facility of the European Union. Author contributions C.T. conceived the study, Á.B.-F. constructed the models, N.C., Á.B.-F. and C.T. made the predictions and prepared the figures, C.T. led the writing of the paper, and all authors critically contributed to the evaluation of the findings and editing the paper. Competing interests The authors declare no competing interests. Additional information Extended data is available for this paper at
https://doi.org/10.1038/s41561-025-01742-z. Supplementary information The online version
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https://doi.org/10.1038/s41561-025-01742-z. Correspondence and requests for materials should be addressed to Csaba Tölgyesi. Peer review information Nature Geoscience thanks the anonymous reviewers for their contribution to the peer review of this work.
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Nature Geoscience Article https://doi.org/10.1038/s41561-025-01742-z Extended Data Table 1 | Predictors used for model construction Predictor type and source Predictor Climatic: WorldClim64 (https://www.worldclim.org/data/bioclim.html)

1. Annual mean temperature (°C ×10)
2. Mean temperature of wettest quarter (°C × 10)
3. Annual precipitation (mm)
4. Precipitation seasonality (coefficient of variation)
5. Precipitation of the driest quarter of the year (mm) Edaphic: SoilGrids250m65 (https://soilgrids.org/)
6. Sand content in the top 15 cm of the soil (%)
7. Soil organic carbon stock (g m−2)
8. Depth to bedrock (m) GMTED201066 (https://topotools.cr.usgs.gov/gmted_viewer)
9. Slope (degree)
10. Elevation above sea level (m)

Nature Geoscience Article https://doi.org/10.1038/s41561-025-01742-z 180° 180° 120° E 120° E 60° E 60° E 0° 0° 60° W 60° W 120° W 120° W 180° 180° 30° N 30° N 0° 0° 30° S 30° S 60° N 60° N 60° S 60° S 100 0 % 180° 180° 120° E 120° E 60° E 60° E 0° 0° 60° W 60° W 120° W 120° W 180° 180° 30° N 30° N 0° 0° 30° S 30° S 60° N 60° N 60° S 60° S 100 0 % 180° 180° 120° E 120° E 60° E 60° E 0° 0° 60° W 60° W 120° W 120° W 180° 180° 30° N 30° N 0° 0° 30° S 30° S 60° N 60° N 60° S 60° S 100 0 % 180° 180° 120° E 120° E 60° E 60° E 0° 0° 60° W 60° W 120° W 120° W 180° 180° 30° N 30° N 0° 0° 30° S 30° S 60° N 60° N 60° S 60° S 100 0 % 180° 180° 120° E 120° E 60° E 60° E 0° 0° 60° W 60° W 120° W 120° W 180° 180° 30° N 30° N 0° 0° 30° S 30° S 60° N 60° N 60° S 60° S 100 0 % 180° 180° 120° E 120° E 60° E 60° E 0° 0° 60° W 60° W 120° W 120° W 180° 180° 30° N 30° N 0° 0° 30° S 30° S 60° N 60° N 60° S 60° S 100 0 % 180° 180° 120° E 120° E 60° E 60° E 0° 0° 60° W 60° W 120° W 120° W 180° 180° 30° N 30° N 0° 0° 30° S 30° S 60° N 60° N 60° S 60° S 100 0 % 180° 180° 120° E 120° E 60° E 60° E 0° 0° 60° W 60° W 120° W 120° W 180° 180° 30° N 30° N 0° 0° 30° S 30° S 60° N 60° N 60° S 60° S 100 0 % Forest Shrubland Grassland Wetland Available for restoration Potential total cover A B C D E F G H Extended Data Fig. 1 | Potential distribution of modelled ecosystems (A-D) and the available area for restoration or spontaneous establishment (E-H), predicted using SSP1-2.6 (2061–2080). Color coding indicates the percentage of each ecosystem type (predicted and available) within a 1 × 1 km grid. Thus, ecosystem combinations (for example forest steppes and savanna-forest mosaics) are also allowed in our grid-level restoration planning, although the proportion of each constituting ecosystem type appears on different maps. For example, a savanna-forest mosaic landscape can contain forested, shrubby and grassy parts within a grid cell, and their proportional values are shown on each of the three corresponding maps. We excluded potential restoration activities in intensive agricultural and built-up areas and in biomes with low productivity (polar and arid regions).