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Lookup NU author(s): Dr Amy GreenORCiD, Dr Selma GuerreiroORCiD, Professor Hayley Fowler
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026. The Author(s). 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.
Author(s): Green AC, Guerreiro SB, Fowler HJ
Publication type: Article
Publication status: Published
Journal: Scientific Data
Year: 2026
Volume: 13
Online publication date: 14/01/2026
Acceptance date: 09/02/2026
Date deposited: 13/04/2026
ISSN (electronic): 2052-4463
Publisher: Springer Nature
URL: https://doi.org/10.1038/s41597-026-06858-4
DOI: 10.1038/s41597-026-06858-4
Data Access Statement: The dataset described in this study is publicly available as part of the GSDR-IDF dataset archived on https://doi.org/10.5281/zenodo.18152624. The data are provided in both.csv and.png format, with comprehensive metadata and example code for filtering also included.
PubMed id: 41690990
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