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The effect of rainfall changes on economic production

Nature volume 601, pages 223–227 (2022)Cite this article

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Abstract

Macro-economic assessments of climate impacts lack an analysis of the distribution of daily rainfall, which can resolve both complex societal impact channels and anthropogenically forced changes1,2,3,4,5,6. Here, using a global panel of subnational economic output for 1,554 regions worldwide over the past 40 years, we show that economic growth rates are reduced by increases in the number of wet days and in extreme daily rainfall, in addition to responding nonlinearly to the total annual and to the standardized monthly deviations of rainfall. Furthermore, high-income nations and the services and manufacturing sectors are most strongly hindered by both measures of daily rainfall, complementing previous work that emphasized the beneficial effects of additional total annual rainfall in low-income, agriculturally dependent economies4,7. By assessing the distribution of rainfall at multiple timescales and the effects on different sectors, we uncover channels through which climatic conditions can affect the economy. These results suggest that anthropogenic intensification of daily rainfall extremes8,9,10 will have negative global economic consequences that require further assessment by those who wish to evaluate the costs of anthropogenic climate change.

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Fig. 1: Assessing the distribution of daily rainfall via thresholds.
Fig. 2: The effect of four rainfall measures on economic growth rates.
Fig. 3: Regional estimates of the historical effect on economic growth rates of a 1-s.d. shock in each of the four rainfall measures.
Fig. 4: Assessing the heterogeneity of the effect of rainfall by income and sector.

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Data availability

The data on economic production and the ERA-5 climate data are both publicly available at https://doi.org/10.5281/zenodo.4681306 and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5, respectively. Secondary data are available at the public repository for this publication: https://doi.org/10.5281/zenodo.5657457. The maps were created using Matplotlib v. 3.4.2 (https://matplotlib.org/), Cartopy v.0.18.0 (Met Office UK, https://pypi.python.org/pypi/Cartopy/0.18.0), Geopandas v. 0.6.1 (https://geopandas.org/) and GADM administrative boundaries (https://gadm.org/). Source data are provided with this paper.

Code availability

The code to reproduce the analysis is available at the public repository for this publication: https://doi.org/10.5281/zenodo.5657457.

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Acknowledgements

We acknowledge funding from the Volkswagen Foundation and from the Horizon 2020 Framework Programme of the European Union (grant agreement number 820712). We thank M. Kalkuhl and S. Lange for discussions regarding economic and climate data, respectively.

Author information

Authors and Affiliations

  1. Research Department of Complexity Science, Potsdam Institute for Climate Impact Research, Potsdam, Germany

    Maximilian Kotz, Anders Levermann & Leonie Wenz

  2. Institute of Physics, Potsdam University, Potsdam, Germany

    Maximilian Kotz & Anders Levermann

  3. Lamont-Doherty Earth Observatory, Columbia University, New York, NY, USA

    Anders Levermann

  4. Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany

    Leonie Wenz

Authors
  1. Maximilian Kotz
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Contributions

M.K. designed and conducted the analysis and contributed to the interpretation and presentation of the results. L.W. proposed the study, contributed to the design of the analysis and to the interpretation and presentation of the results. A.L. contributed to the interpretation and presentation of the results.

Corresponding author

Correspondence to Leonie Wenz.

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Competing interests

The authors declare no competing interests.

Peer review information

Nature thanks Xin-Zhong Liang, Chad W. Thackeray 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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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Historical means of the four principal rainfall measures.

Maps of the historical (1979–2019) means of each annual rainfall measure. a, The annual total rainfall. b, The monthly rainfall deviations (a weighted annual sum of anomalies of monthly rainfall from their climatological means which are, by definition, zero mean). c, The number of wet days. d, The extreme daily rainfall measure (the annual sum of rainfall on days exceeding the 99.9th percentile of the historical distribution).

Extended Data Fig. 2 Historical variability of the four principal rainfall measures.

Historical variability (the standard deviation of annual values over the years 1979–2019) for each measure of rainfall.

Extended Data Fig. 3 Rich and poor differentiated response of economic growth to changes in rainfall.

As Fig. 2 but having estimated economic responses to rainfall for rich and poor countries separately.

Extended Data Fig. 4 Response of sectoral growth to changes in rainfall.

As Fig. 2 but having estimated economic responses to rainfall for the agricultural (“ag”), manufacturing (“man”) and services (“serv”) sectors separately.

Extended Data Table 1 Results of the main econometric specification for the effect of temperature and rainfall changes on economic growth rates
Full size table

Supplementary information

Supplementary Information

This Supplementary Information contains: Supplementary Sections 1–3, Figs. 1–6, Tables 1–17 and additional references.

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Kotz, M., Levermann, A. & Wenz, L. The effect of rainfall changes on economic production. Nature 601, 223–227 (2022). https://doi.org/10.1038/s41586-021-04283-8

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