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Uncovering the hidden role of peri-urban vegetation in modulating urban precipitation

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Abstract

Peri-urban vegetation influences urban hydroclimates, yet its role in shaping urban precipitation remains understudied due to binary urban–non-urban framings, the limited representation of peri-urban landscapes in models and datasets, and a predominant focus on intra-urban areas. Here we integrate satellite-derived vegetation trends with an evapotranspiration model and an atmospheric moisture-tracking model to quantify how peri-urban vegetation change affects urban precipitation within 1,029 cities worldwide. We identify a spatially coupled hydroclimatic mechanism in which vegetation-driven shifts in peri-urban evapotranspiration modulate urban precipitation via atmospheric moisture transfer. Although these changes contribute only 1.9% of annual urban precipitation, they account for 18.3% of its long-term increase, indicating a disproportionate and systematic influence on urban hydroclimate trajectories. We further find that this coupling strengthens in cities with more abundant surrounding vegetation, wind-aligned greening and lower background humidity. Our findings clarify how peri-urban land–atmosphere interactions regulate urban climates and highlight the need to integrate peri-urban ecosystems into climate-resilience planning.

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Fig. 1: Conceptual illustration of the hypothesis that peri-urban vegetation change modifies ET, thereby affecting urban precipitation through moisture transport.
Fig. 2: Trends in urban precipitation and peri-urban vegetation from 2000 to 2021.
Fig. 3: Peri-urban ET variation associated with peri-urban vegetation change.
Fig. 4: Urban precipitation variation associated with peri-urban vegetation change through ET and atmospheric moisture transport.
Fig. 5: City-specific factors shape RPR and RPR-T.

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Data availability

All datasets used in this study are publicly available. Urban boundaries were based on the Global Urban Boundary dataset (GUB, https://data-starcloud.pcl.ac.cn/iearthdata/14). Precipitation data were obtained from the Multi-Source Weighted-Ensemble Precipitation dataset (MSWEP v2.8, https://www.gloh2o.org/mswep/). Vegetation dynamics were derived from the GLOBMAP LAI v3 product (https://zenodo.org/records/4700264)55. Meteorological inputs were sourced from the ERA5-Land monthly averaged data (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means). Moisture transport linkages were assessed using UTrack-atmospheric-moisture (https://doi.org/10.1594/PANGAEA.912710). Validation of the ET simulations was performed using the FLUXNET2015 dataset (https://doi.org/10.6084/m9.figshare.12295910)74, the harmonized gap-filled dataset from 20 urban flux tower sites (https://zenodo.org/records/7104984)75, the Global Land Evaporation Amsterdam Model (GLEAM, https://zenodo.org/records/14724263)76 and ERA5-Land monthly ET (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means). Validation of the precipitation dataset used in this study was performed using a global collection of ground-based precipitation observations from 1929 to 2024 from the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (https://www.ncei.noaa.gov/data/global-summary-of-the-day/access/) and the Global Precipitation Measurement dataset (GPM, https://doi.org/10.5065/SRTD-0R12). Validation of the LAI dataset used in this study was performed using Global Land Surface Satellite (GLASS, https://www.glass.hku.hk/download.html) and Global Inventory Modeling and Mapping Studies (GIMMS, https://doi.org/10.5281/zenodo.7649107) LAI data. Atmospheric CO2 concentrations used in this study were obtained from globally averaged marine surface annual mean CO2 data (https://gml.noaa.gov/ccgg/trends/data.html). Climate classifications were derived from the Köppen–Geiger system (https://www.gloh2o.org/koppen/). Land-cover types were identified using the global land-cover product with fine classification system at 30 m using time-series Landsat imagery (GLC_FCS30, https://zenodo.org/records/3986872). Urban economic sectors were categorized using the Global Sectoral GDP map at 30′′ resolution (SectGDP30, https://zenodo.org/records/13991673). City topography was extracted from GEBCO_2021 Grid (https://download.gebco.net/). Aridity indices were sourced from the Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3 (https://doi.org/10.6084/m9.figshare.7504448.v5). Information regarding the PT-JPLim ET model used in this study has been made publicly available. For any inquiries regarding the model, please contact the corresponding author.

Code availability

The code used to perform the analysis in this study is available at https://github.com/LaianLaian/peri-urban-Vegetation-urban-Precipitation, completed with a comprehensive introduction and the basic data.

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Acknowledgements

This study was supported by grants from the Research Fund of the State Key Laboratory of Water Cycle and Water Security (grant no. SKL2025RCPY02, to W.S.), the Beijing Municipal Natural Science Foundation (grant no. 8222036, to W.S.) and the National Natural Science Foundation of China (grant nos. U2240223 and 51979285, to W.S.).

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Author notes

  1. These authors contributed equally: Rui Shao, Jiaqi Li.

Authors and Affiliations

  1. State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing, China

    Rui Shao, Jiaqi Li, Weiwei Shao, Yicheng Wang, Zhiyong Yang, Jiahong Liu, Yong Zhao, Jianhua Wang & Hao Wang

  2. Department of Hydraulic Engineering, Tsinghua University, Beijing, China

    Rui Shao, Dawen Yang, Guangheng Ni & Yuting Yang

  3. Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore

    Rui Shao

  4. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China

    Dawen Yang, Guangheng Ni & Yuting Yang

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  1. Rui Shao
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Contributions

R.S., J.L. and W.S. conceptualized the study. R.S., J.L. and W.S. drafted the paper. R.S., J.L. and W.S. conducted the data analysis. W.S., Y.W., D.Y., G.N., Y.Y., Z.Y. and J.L. supported the data analysis and edited the paper. D.Y., G.N., Y.Y., Z.Y, J.L, Y.Z., J.W. and H.W. contributed to the design, research questions and dataset selection. All authors discussed the results and contributed to the final revision of the paper.

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Correspondence to Weiwei Shao.

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Nature Cities thanks Harro J. Jongen, Shuo Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Placebo test for validating the robustness of the LAIperi–Pur association.

Distribution of regression coefficients (β, x-axis) and corresponding two-sided p-values (red circles, left y-axis) obtained from 1,000 randomized placebo tests, and the relationship between peri-urban vegetation change (LAIₚₑᵣᵢ) and urban precipitation change (Pur) is randomly permuted in time within each city. The blue line represents the kernel density (right y-axis) of the placebo-derived coefficients, approximating the empirical null distribution centered around zero. The vertical dashed red line indicates the observed regression coefficient from the actual data (β = 0.2096), which lies far outside the 95% confidence interval of the placebo distribution (–0.014 to 0.013). This result confirms that the observed LAIperi–Pur relationship is highly unlikely to arise from random temporal coincidence (two-sided empirical permutation p-value = 0.0010, based on n = 1,000 permutations), which demonstrates the statistical robustness of the coupling signal. The statistical analysis is performed using a linear panel regression model and the significance of β is assessed with a two-sided t-test (n = 22,638 site-year observations; df = 22,636; t = 32.25; 95% CI of β: 0.1969 to 0.2224). No adjustment for multiple comparisons is applied.

Source data

Extended Data Fig. 2 The framework of our methodology.

a, Selection of 1,029 cities and corresponding peri-urban areas. b, Confirmation of the association between peri-urban vegetation (LAIperi) change and urban precipitation (Pur) change. c, Quantifying of the vegetation change-induced peri-urban ET change (ΔETperi) using the PT-JPLim model. d, Quantifying the contribution of peri-urban ET change to urban precipitation. e, Identifying the city-specific factors which shape spatial variability in vegetation–precipitation coupling.

Extended Data Fig. 3 Conceptual illustration of ΔETperi under vegetation change scenarios design.

The figure demonstrates the scenario design used to calculate ΔETperi, defined as the difference in peri-urban ET between the real- (green line with circle) and the fixed-vegetation scenario (brown line with square). ΔETperi quantifies the additional ET induced by peri-urban vegetation change.

Extended Data Fig. 4 Conceptual illustration of the UTrack model-based calculation of urban precipitation sourced from peri-urban evapotranspiration change.

The figure illustrates the conceptual workflow used to estimate the urban precipitation contribution from ETperi based on the UTrack atmospheric moisture tracking model. For each peri-urban source grid (i, j), the ET flux (\(\Delta \mathrm{ET}_{\mathrm{peri}}^{(i,j)}\)) is multiplied by the moisture transport fraction (\({U}^{(i,\,j)}\)) to calculate its contribution to precipitation in the urban target grid (\(\Delta P_{\mathrm{ur}}^{(i,j)} = \Delta \mathrm{ET}_{\mathrm{peri}}^{(i,j)} \times U^{(i,j)}\)). This approach enables the quantification of how vegetation change-driven ET in peri-urban areas affects urban precipitation.

Extended Data Fig. 5 Illustration of overlapping peri-urban areas and corresponding UTrack-derived moisture transport ratios.

a, Example of two adjacent cities (City 215 and City 216) with partially overlapping peri-urban areas. Intra-urban areas are shown in yellow, peri-urban areas of City 216 in green, and those of City 215 in blue. The cyan area indicates the overlapping peri-urban areas. b, Spatial distribution of UTrack-derived moisture transport ratios (U) within the overlapping area for each city (unit: 10−2%).

Extended Data Fig. 6 Directional alignment between the dominant axis of peri-urban vegetation change and the prevailing wind direction.

a, Example of a ΔLAI ∥ WIND city, where the dominant climatological wind direction (Scity) aligns with the direction of the strongest LAI change in peri-urban areas (S*). b, Example of a ΔLAI ∠ WIND city, where Scity does not align with S*. The city and paired peri-urban area are divided into four quadrants (North, South, East, and West), and alignment is determined by comparing Scity with S*.

Extended Data Table 1 Statistical summaries for differences in median RPR between city groups
Full size table
Extended Data Table 2 Statistical summaries for differences in median RPR-T between city groups
Full size table

Supplementary information

Source data

44284_2026_416_MOESM4_ESM.xlsx (download XLSX )

Source Data Figs. 2–5 Statistical source data used to generate all panels of Fig. 2 (sheet Fig_2), Fig. 3 (sheet Fig_3), Fig. 4 (sheet Fig_4) and Fig. 5 (sheet Fig_5). Source Data Extended Data Fig. 1 Statistical source data used to generate all panels of Extended Data Fig.1 (sheet ED_Fig_1). Source Data Extended Data Tables. 1 and 2 Statistical source data used to generate all panels of Extended Data Table 1 (sheet ED_Table_1) and Extended Data Table 2 (sheet ED_Table_2).

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Shao, R., Li, J., Shao, W. et al. Uncovering the hidden role of peri-urban vegetation in modulating urban precipitation. Nat Cities (2026). https://doi.org/10.1038/s44284-026-00416-0

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