Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Rainfall variability and under-five child mortality in 59 low- and middle-income countries

Nature Water volume 3, pages 881–889 (2025)Cite this article

Subjects

Abstract

Climate change is reshaping the Earth’s hydrological cycle. Such changes impact children’s health through multiple pathways. Here we show that, in 59 low- and middle-income countries, although sufficient annual rainfall decreases under-five child mortality, anomalies in seasonal rainfall could increase under-five mortality. The risk associated with rainfall scarcity (odds ratio 1.15, 95% confidence interval (CI) 1.11–1.20) was much higher than that associated with rainfall surplus (odds ratio 1.04, 95% CI 1.02–1.06). Extreme rainfall amounts and the number of wet days are positively associated with elevated under-five child mortality. These risks were more pronounced for children from rural areas, families with lower educational attainment and households that depend on natural water sources. From 2000 to 2020, rainfall variations, extreme daily rainfall events and the number of wet days are estimated to cause 290 under-five child deaths per 10,000 persons annually (95% CI 177– 417). This investigation provides important insights into the overlooked health consequences of rainfall pattern changes on vulnerable populations.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Historical averages of key measures of rainfall distribution (1980–2020).
Fig. 2: The estimated exposure–response relationship between four rainfall distribution measures and risk of mortality in under-five children.
Fig. 3: OR of mortality risk in under-five children with different rainfall distribution measures.
Fig. 4: Average annual excess mortality in under-five children (per 10,000) associated with four rainfall distribution measures by five regions (2000–2020).

Similar content being viewed by others

Data availability

Survey data, including child death records and socio-economic data, in this study are publicly available upon request from the Demographic and Health Surveys Program (https://dhsprogram.com/). Data from ERA5 are available at https://cds.climate.copernicus.eu. Source data are provided with this paper.

Code availability

Custom code that supports the findings of this study is available from the corresponding authors upon request.

References

  1. Rosenzweig, C., Tubiello, F. N., Goldberg, R., Mills, E. & Bloomfield, J. Increased crop damage in the US from excess precipitation under climate change. Glob. Environ. Change 12, 197–202 (2002).

    Article  Google Scholar 

  2. Kotz, M., Levermann, A. & Wenz, L. The effect of rainfall changes on economic production. Nature 601, 223–227 (2022).

    Article  PubMed  CAS  Google Scholar 

  3. Degroot, D. et al. The history of climate and society: a review of the influence of climate change on the human past. Environ. Res. Lett. 17, 103001 (2022).

    Article  Google Scholar 

  4. Liu, J., Wang, B., Cane, M. A., Yim, S.-Y. & Lee, J.-Y. Divergent global precipitation changes induced by natural versus anthropogenic forcing. Nature 493, 656–659 (2013).

    Article  PubMed  CAS  Google Scholar 

  5. O’Gorman, P. A. & Schneider, T. The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc. Natl Acad. Sci. USA 106, 14773–14777 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Ham, Y.-G. et al. Anthropogenic fingerprints in daily precipitation revealed by deep learning. Nature 622, 301–307 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Mondal, S. K. et al. Changes in extreme precipitation across South Asia for each 0.5 °C of warming from 1.5 °C to 3.0 °C above pre-industrial levels. Atmos. Res. 266, 105961 (2022).

    Article  CAS  Google Scholar 

  8. Fallah, B. et al. Anthropogenic influence on extreme temperature and precipitation in Central Asia. Sci. Rep. 13, 6854 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Konapala, G., Mishra, A. K., Wada, Y. & Mann, M. E. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat. Commun. 11, 3044 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Milly, P. C. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).

    Article  Google Scholar 

  11. Kumar, S., Allan, R. P., Zwiers, F., Lawrence, D. M. & Dirmeyer, P. A. Revisiting trends in wetness and dryness in the presence of internal climate variability and water limitations over land. Geophys. Res. Lett. 42, 10,867–810,875 (2015).

    Article  Google Scholar 

  12. Wang, P., Asare, E., Pitzer, V. E., Dubrow, R. & Chen, K. Associations between long-term drought and diarrhea among children under five in low-and middle-income countries. Nat. Commun. 13, 3661 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Grace, K., Davenport, F., Hanson, H., Funk, C. & Shukla, S. Linking climate change and health outcomes: examining the relationship between temperature, precipitation and birth weight in Africa. Glob. Environ. Change 35, 125–137 (2015).

    Article  Google Scholar 

  14. Uttajug, A., Ueda, K., Seposo, X. & Francis, J. M. Association between extreme rainfall and acute respiratory infection among children under-5 years in sub-Saharan Africa: an analysis of Demographic and Health Survey data, 2006–2020. BMJ Open 13, e071874 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  15. He, C. et al. Flood exposure and pregnancy loss in 33 developing countries. Nat. Commun. 15, 20 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Yeboah, E. et al. Every drop matters: combining population-based and satellite data to investigate the link between lifetime rainfall exposure and chronic undernutrition in children under five years in rural Burkina Faso. Environ. Res. Lett. 17, 054027 (2022).

    Article  Google Scholar 

  17. Cooper, M., Brown, M. E., Azzarri, C. & Meinzen-Dick, R. Hunger, nutrition, and precipitation: evidence from Ghana and Bangladesh. Popul. Environ. 41, 151–208 (2019).

    Article  Google Scholar 

  18. Cornwell, K. & Inder, B. Child health and rainfall in early life. J. Dev. Stud. 51, 865–880 (2015).

    Article  Google Scholar 

  19. Shively, G. E. Infrastructure mitigates the sensitivity of child growth to local agriculture and rainfall in Nepal and Uganda. Proc. Natl Acad. Sci. USA 114, 903–908 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Asmall, T. et al. The adverse health effects associated with drought in Africa. Sci. Total Environ. 793, 148500 (2021).

    Article  PubMed  CAS  Google Scholar 

  21. Suhr, F. & Steinert, J. I. Epidemiology of floods in sub-Saharan Africa: a systematic review of health outcomes. BMC Public Health 22, 268 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Olayide, O. E. & Alabi, T. Between rainfall and food poverty: assessing vulnerability to climate change in an agricultural economy. J. Clean. Prod. 198, 1–10 (2018).

    Article  Google Scholar 

  23. Dimitrova, A., McElroy, S., Levy, M., Gershunov, A. & Benmarhnia, T. Precipitation variability and risk of infectious disease in children under 5 years for 32 countries: a global analysis using Demographic and Health Survey data. Lancet Planet. Health 6, e147–e155 (2022).

    Article  PubMed  Google Scholar 

  24. Mbouna, A. D. et al. Modelled and observed mean and seasonal relationships between climate, population density and malaria indicators in Cameroon. Malar. J. 18, 1–14 (2019).

    Article  Google Scholar 

  25. Hii, Y. L., Zhu, H., Ng, N., Ng, L. C. & Rocklöv, J. Forecast of dengue incidence using temperature and rainfall. PLoS Negl. Trop. Dis. 6, e1908 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Buchner, H. & Rehfuess, E. A. Cooking and season as risk factors for acute lower respiratory infections in African children: a cross-sectional multi-country analysis. PLoS ONE 10, e0128933 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Mensch, B. S., Chuang, E. K., Melnikas, A. J. & Psaki, S. R. Evidence for causal links between education and maternal and child health: systematic review. Trop. Med. Int. Health 24, 504–522 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Novaes, C., Silva Pinto, F. & Marques, R. C. Aedes aegypti—insights on the impact of water services. Geohealth 6, e2022GH000653 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Sinisi, L. & Aertgeerts, R. Guidance on Water Supply and Sanitation in Extreme Weather Events (World Health Organization Regional Office for Europe, 2011).

  30. Zhang, W. et al. Increasing precipitation variability on daily-to-multiyear time scales in a warmer world. Sci. Adv. 7, eabf8021 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  Google Scholar 

  32. Bell, B. et al. The ERA5 global reanalysis: preliminary extension to 1950. Q. J. R. Meteorol. Soc. 147, 4186–4227 (2021).

    Article  Google Scholar 

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

    Google Scholar 

  34. Kummerow, C., Barnes, W., Kozu, T., Shiue, J. & Simpson, J. The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol. 15, 809–817 (1998).

    Article  Google Scholar 

  35. Maggioni, V., Meyers, P. C. & Robinson, M. D. A review of merged high-resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) era. J. Hydrometeorol. 17, 1101–1117 (2016).

    Article  Google Scholar 

  36. Corsi, D. J., Neuman, M., Finlay, J. E. & Subramanian, S. Demographic and health surveys: a profile. Int. J. Epidemiol. 41, 1602–1613 (2012).

    Article  PubMed  Google Scholar 

  37. Aliaga, A. & Ren, R. The Optimal Sample Sizes for Two-Stage Cluster Sampling in Demographic and Health Surveys (ORC Macro, 2006).

  38. Burgert, C. R., Colston, J., Roy, T. & Zachary, B. Geographic Displacement Procedure and Georeferenced Data Release Policy for the Demographic and Health Surveys (ICF International, 2013).

  39. Holtermann, L. Precipitation anomalies, economic production, and the role of “first-nature” and “second-nature” geographies: a disaggregated analysis in high-income countries. Glob. Environ. Change 65, 102167 (2020).

    Article  Google Scholar 

  40. Xue, T. et al. Associations between exposure to landscape fire smoke and child mortality in low-income and middle-income countries: a matched case–control study. Lancet Planet. Health 5, e588–e598 (2021).

    Article  PubMed  Google Scholar 

  41. Xue, T., Zhu, T., Geng, G. & Zhang, Q. Association between pregnancy loss and ambient PM2·5 using survey data in Africa: a longitudinal case–control study, 1998–2016. Lancet Planet. Health 3, e219–ee225 (2019).

    Article  PubMed  Google Scholar 

  42. Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Bombardi, R. J., Kinter, J. L. & Frauenfeld, O. W. A global gridded dataset of the characteristics of the rainy and dry seasons. Bull. Am. Meteorol. Soc. 100, 1315–1328 (2019).

    Article  Google Scholar 

  44. Li, X. et al. Mapping global urban boundaries from the global artificial impervious area (GAIA) data. Environ. Res. Lett. 15, 094044 (2020).

    Article  Google Scholar 

  45. Steenland, K., Seals, R., Klein, M., Jinot, J. & Kahn, H. D. Risk estimation with epidemiologic data when response attenuates at high-exposure levels. Environ. Health Perspect. 119, 831–837 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Rhomberg, L. R., Chandalia, J. K., Long, C. M. & Goodman, J. E. Measurement error in environmental epidemiology and the shape of exposure-response curves. Crit. Rev. Toxicol. 41, 651–671 (2011).

    Article  PubMed  Google Scholar 

  47. Zhu, Y. et al. Global warming may significantly increase childhood anemia burden in sub-Saharan Africa. One Earth 6, 1388–1399 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant no. 82430105), Shanghai B&R Joint Laboratory Project (grant no. 22230750300), Shanghai Municipal Science and Technology Major Project (grant no. 2023SHZDZX02), Shanghai International Science and Technology Partnership Project (grant no. 21230780200) and the Shanghai 3-year Public Health Action Plan (grant no. GWVI-11.1-39). C.H. is supported by the Alexander von Humboldt Foundation.

Author information

Author notes

  1. These authors contributed equally: Cheng He, Yixiang Zhu.

Authors and Affiliations

  1. School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, NHC Key Lab of Health Technology Assessment, IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China

    Cheng He, Yixiang Zhu, Yichen Guo, Renjie Chen & Haidong Kan

  2. Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany

    Cheng He

  3. School of the Environment, Yale University, New Haven, CT, USA

    Michelle L. Bell

  4. Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK

    Veronique Filippi

  5. Royal Meteorological Society, Reading, UK

    Chloe Brimicombe

  6. Children’s Hospital of Fudan University, National Center for Children’s Health, Shanghai, China

    Haidong Kan

Authors
  1. Cheng He
    View author publications

    Search author on:PubMed Google Scholar

  2. Yixiang Zhu
    View author publications

    Search author on:PubMed Google Scholar

  3. Yichen Guo
    View author publications

    Search author on:PubMed Google Scholar

  4. Michelle L. Bell
    View author publications

    Search author on:PubMed Google Scholar

  5. Veronique Filippi
    View author publications

    Search author on:PubMed Google Scholar

  6. Chloe Brimicombe
    View author publications

    Search author on:PubMed Google Scholar

  7. Renjie Chen
    View author publications

    Search author on:PubMed Google Scholar

  8. Haidong Kan
    View author publications

    Search author on:PubMed Google Scholar

Contributions

C.H., Y.Z., R.C. and H.K. contributed to study conceptualization. C.H., Y.Z., R.C. and H.K. contributed to the study methods. C.H., Y.Z. and Y.G. did the formal analysis. C.H., Y.Z., Y.G., R.C. and H.K. contributed to data curation and collection of the mortality database. C.H., Y.Z. and Y.G. contributed to the making of all the figures and tables. C.H., Y.Z., R.C. and H.K. contributed to the study draft preparation. C.H., Y.Z., Y.G., M.L.B., V.F., C.B., R.C. and H.K. contributed to the study revision preparation. R.C. and H.K. supervised all the data analysis and paper writing. All authors reviewed and edited the paper and approved its submission.

Corresponding authors

Correspondence to Renjie Chen or Haidong Kan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Water thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Average annual total excess mortality in children under-five associated with four rainfall distribution measures across 59 Low- or Middle-Income Countries (2000-2020).

including the mean annual total rainfall (a), the long-term average of the standard deviations for monthly rainfall totals (b), the average annual total of rainfall recorded on days exceeding the 99.9th percentile of the historical rainfall distribution (c), and the average annual days classified as ‘wet day’ (daily rainfall exceeding 1 mm) (d)The analysis employs a spatial resolution of 0.25-degree latitude-longitude grid. Population counts for children under five at each grid cell were sourced from Worldpop, and baseline child mortality rates were obtained from the United Nations International Children’s Emergency Fund (UNICEF). See the methods section for a detailed explanation.

Source data

Extended Data Fig. 2 Average annual total excess mortality in children under-five attributable to monthly rainfall deviations across 59 Low- or Middle-Income Countries (2000-2020).

This analysis bifurcates the effects of Monthly Rainfall Deviations from the main analysis into two distinct categories: (a) positive rainfall deviations (Monthly Rainfall Deviations < 0) representing drier than average conditions, and (b) negative rainfall deviations (Monthly Rainfall Deviations > 0) indicating wetter than average conditions.

Source data

Extended Data Fig. 3 Average annual excess mortality in children under-five (per 10,000) associated with monthly rainfall deviations by five regions (2000-2020).

The regional outcomes are derived from evaluations conducted in subsets of the 59 study countries. The color bars represent the estimated mortality in children under five associated with each side of monthly rainfall deviations; the white boxes denote the 95% confidence intervals.

Source data

Supplementary information

Supplementary Information

Supplementary methods, Figs. 1–5, discussion and Tables 1–14.

Reporting Summary

Source data

Source Data Fig. 1

Unprocessed western blots and/or gels.

Source Data Fig. 2

Unprocessed western blots and/or gels.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 1

Unprocessed western blots and/or gels.

Source Data Extended Data Fig. 2

Unprocessed western blots and/or gels.

Source Data Extended Data Fig. 3

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, C., Zhu, Y., Guo, Y. et al. Rainfall variability and under-five child mortality in 59 low- and middle-income countries. Nat Water 3, 881–889 (2025). https://doi.org/10.1038/s44221-025-00478-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s44221-025-00478-9

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene