Abstract
Short-duration extreme rainfall events can cause flash flooding and infrastructure failures, yet resources to assess these remain limited, particularly at the global scale. Heterogeneous data availability, inconsistent quality control, and methodological differences hinder the development of comparable intensity-duration-frequency (IDF) estimates. To address this gap, we present GSDR-IDF, a global dataset of intensity-duration-frequency curves derived from the largest quality-controlled sub-daily rain gauge dataset: the Global Sub-Daily Rainfall dataset (GSDR), comprising +24,000 hourly rain gauge records for all major climate regions. We apply robust extreme value analysis methods, including single-gauge and regional frequency approaches, to estimate return levels for 1-, 3-, 6- and 24-hour durations and for 10-, 30-, and 100-year return levels. These are then combined to give IDF curves for each rain gauge, providing an openly accessible, traceable, and reproducible resource for hydrological modelling, engineering design, flood-risk assessment and climate-resilience planning. This dataset represents a step change in accessibility and precision for global IDF estimation and enables a wide range of cross-disciplinary applications.
Similar content being viewed by others
Data availability
The dataset described in this study is publicly available as part of the GSDR-IDF dataset archived on https://doi.org/10.5281/zenodo.1815262433. The data are provided in both.csv and.png format, with comprehensive metadata and example code for filtering also included.
Code availability
The code used in this study is publicly available as part of the GSDR-IDF dataset archived on Zenodo: Green, A. C. (2026). GSDR-IDF: Global Intensity-Duration-Frequency curves based on observed sub-daily rainfall [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1815262433
References
Fowler, H. et al. Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes. Phil. Trans. R. Soc. A 379, 2195, https://doi.org/10.1098/rsta.2019.0542 (2021).
Fowler, H. J. et al. Precipitation extremes in 2023. Nat. Rev. Earth Environ. 5, 250–252, https://doi.org/10.1038/s43017-024-00547-9 (2024).
Green, A. C. et al. Precipitation extremes in 2024. Nat. Rev. Earth Environ. 6, 243–245, https://doi.org/10.1038/s43017-025-00666-x (2025).
Hettiarachchi, S., Wasko, C. & Sharma, A. Increase in flood risk resulting from climate change in a developed urban watershed – the role of storm temporal patterns. Hydrol. Earth Syst. Sci. 22, 2041–2056, https://doi.org/10.5194/hess-22-2041-2018 (2018).
Tapiador, F. et al. Global precipitation measurements for validating climate models. Atmos. Res. 197, 1–20, https://doi.org/10.1016/j.atmosres.2017.06.021 (2017).
Dunn, R. E., Fowler, H. J., Green, A. C. & Lewis, E. Tipping-bucket rain gauges: a review of the undercatch phenomenon, and methods for its reduction and correction. Weather. 80, 196–205, https://doi.org/10.1002/wea.7736 (2025).
Blenkinsop, S. et al. The INTENSE project: using observations and models to understand the past, present and future of sub-daily rainfall extremes. Adv. Sci. Res. 15, 117–126, https://doi.org/10.5194/asr-15-117-2018 (2018).
Lewis, E. et al. GSDR: A Global Sub-Daily Rainfall Dataset. J. Clim. 32, 4715–4729, https://doi.org/10.1175/JCLI-D-18-0143.1 (2019).
Lewis, E. et al. Subset of Global Sub-Daily Rainfall (GSDR) dataset [Data set]. Zenodo https://doi.org/10.5281/zenodo.8369987 (2019).
Lewis, E. et al. Quality control of a global hourly rainfall dataset. Environ. Model. Softw. 144, 105169, https://doi.org/10.1016/j.envsoft.2021.105169 (2021).
Coles, S., Bawa, J., Trenner, L. & Dorazio, P. An Introduction to Statistical Modeling of Extreme Values, 2nd ed. Springer, London, UK (2001).
Hosking, J.R.M. & Wallis, J.R. Regional Frequency Analysis, IBM T J Watson Research Center, New York, James R. Wallis (1997).
Liang, Y. et al. L-Moment-Based Regional Frequency Analysis of Annual Extreme Precipitation and its Uncertainty Analysis. Water Resour. Manage. 31, 3899–3919, https://doi.org/10.1007/s11269-017-1715-5 (2017).
Debbarma, N. et al. Uncertainty Analysis of Regional Rainfall Frequency Estimates in Northeast India. Civil Eng. J. 7(11), 1817–1835, https://doi.org/10.28991/cej-2021-03091762 (2021).
Noor, M. et al. Evaluating intensity-duration-frequency (IDF) curves of satellite-based precipitation datasets in Peninsular Malaysia. Atmos. Res. 248, 105203, https://doi.org/10.1016/j.atmosres.2020.105203 (2021).
Zeri, S. J., Hamed, M. M., Wang, X. & Shahid, S. Utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in Iraq. Water 15, 852, https://doi.org/10.3390/w15050852 (2023).
Courty, L. et al. Intensity-duration-frequency curves at the global scale. Environ. Res. Lett. 14, 084045, https://doi.org/10.1088/1748-9326/ab370a (2018).
Guerreiro, S. B. et al. Unravelling the complex interplay between daily and sub-daily rainfall extremes in different climates. Weather Clim. Extremes 46, 100735, https://doi.org/10.1016/j.wace.2024.100735 (2024).
Lau, A. & Behrangi, A. Understanding Intensity–Duration–Frequency (IDF) Curves Using IMERG Sub-Hourly Precipitation against Dense Gauge Networks. Remote Sens. 14, 5032, https://doi.org/10.3390/rs14195032 (2022).
Papalexiou, S. M. & Koutsoyiannis, D. Battle of extreme value distributions: A global survey on extreme daily rainfall. Water Resour. Res. 49, 187–201, https://doi.org/10.1002/wrcr.20073 (2013).
Hosking, J.R.M. & Wallis, J.R. Regional frequency analysis using L-moments. In Hydraulic Engineering: Saving a Threatened Resource—In Search of Solutions, 13–18. ASCE, Baltimore, Maryland (2019).
Pritchard, D. et al. An Observation-Based Dataset of Global Sub-Daily Precipitation Indices (GSDR-I). Sci. Data 10, 393, https://doi.org/10.1038/s41597-023-02238-4 (2023).
Pritchard, D. et al. GSDR-I Global Sub-Daily Precipitation Indices - Dataset (0.1.0) [Data set]. Zenodo https://doi.org/10.5281/zenodo.7492812 (2022).
Chaudhuri, R. & Sharma, P. Addressing uncertainty in extreme rainfall intensity for semi-arid urban regions: case study of Delhi, India. Nat. Hazards 104, 2307–2324, https://doi.org/10.1007/s11069-020-04273-5 (2020).
Pansera, W. & Gomes, B. Performance of multiparameter distributions in estimating rainfall extremes and deriving IDF equations in Paraná. Rev. Bras. Recur. Hídricos 30(3), 2318–0331, https://doi.org/10.1590/2318-0331.302520240060 (2025).
Ballarin, A. et al. Combined predictive and descriptive tests for extreme rainfall probability distribution selection. Hydrol. Sci. J. 67, 1130–1140, https://doi.org/10.1080/02626667.2022.2063725 (2022).
Gentilucci, M. et al. GEV Analysis of Extreme Rainfall: Comparing Different Time Intervals to Analyse Model Response in Terms of Return Levels in the Study Area of Central Italy. Sustainability 15(15), 11656, https://doi.org/10.3390/su151511656 (2023).
Blanchet, J. et al. A regional GEV scale-invariant framework for Intensity-Duration-Frequency analysis. J. Hydrol. 540, 82–95, https://doi.org/10.1016/j.jhydrol.2016.06.007 (2016).
Kyojo, E. et al. Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania. Sci. Afr. 25, 2321, https://doi.org/10.1016/j.sciaf.2024.e02321 (2024).
Ghalavand, M. et al. Modeling Return Levels of Non-Stationary Rainfall Extremes Due to Climate Change. Atmosphere 16(2), 136, https://doi.org/10.3390/atmos16020136 (2025).
Hossain, I. et al. Comparison of estimation techniques for generalised extreme value (GEV) distribution parameters: a case study with Tasmanian rainfall. Int. J. Environ. Sci. Technol. 19, 7737–7750, https://doi.org/10.1007/s13762-021-03693-5 (2021).
Kjeldsen, T. R. et al. Revitalisation of the FSR/FEH rainfall-runoff method. Final Report to DEFRA/EA. Wallingford (2005).
Green, A. C. et al. GSDR-IDF: Global Intensity-Duration-Frequency curves based on observed sub-daily rainfall. Zenodo https://doi.org/10.5281/zenodo.18152624 (2026).
Acknowledgements
This work was supported by the INTENSE project, funded through the European Research Council (grant ID: ERC-2013-CoG-617329), and the IMPETUS4CHANGE (I4C) project (grant agreement ID: 101081555) as part of HORIZON-CL5-2022-D1-02-04. Additional support was provided by the Co-Centre for Climate + Biodiversity + Water, managed by Research Ireland, Northern Ireland’s Department of Agriculture, Environment and Rural Affairs (DAERA) and funded by UK Research and Innovation (UKRI) via the UK’s International Science Partnerships Fund (ISPF) and the Irish Government’s Shared Island initiative (grant ID: NE/Y006496/1).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Green, A.C., Guerreiro, S.B. & Fowler, H.J. Global Intensity-Duration-Frequency curves based on observed sub-daily rainfall (GSDR-IDF). Sci Data (2026). https://doi.org/10.1038/s41597-026-06858-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41597-026-06858-4
