Articles | Volume 28, issue 2
https://doi.org/10.5194/hess-28-375-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-375-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jan 2024
Research article |
| 31 Jan 2024
| 31 Jan 2024
Predicting extreme sub-hourly precipitation intensification based on temperature shifts
Department of Geosciences, University of Padova, Padua, Italy
Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy
Marika Koukoula
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Antonio Canale
Department of Statistical Sciences, University of Padova, Padua, Italy
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
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- Urbanization Enhances Shorter‐Duration Precipitation Intensity in the Yangtze River Delta Region X. Xie et al. https://doi.org/10.1029/2024JD043300
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- Enhancing generalizability of data-driven urban flood models by incorporating contextual information T. Cache et al. https://doi.org/10.5194/hess-28-5443-2024
- Development of intensity-duration-frequency curves using machine learning and satellite-derived precipitation data E. Dargham & C. Andraos https://doi.org/10.3389/frwa.2026.1727182
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- Record-breaking rainfall: a stochastic approach for its prediction M. Chen et al. https://doi.org/10.1038/s44304-025-00148-6
- Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh A. Dey & M. Patwary https://doi.org/10.1007/s00477-025-02969-3
- Estimation of future rainfall extreme values by temperature-dependent disaggregation of climate model data N. Ebers et al. https://doi.org/10.5194/nhess-24-2025-2024
- Evolving patterns of hourly rainfall extremes across seasonal regimes in Chennai, India P. Bheeman & S. Rangarajan https://doi.org/10.1007/s11600-026-01803-w
- Benefits of the simplified MEV for analyzing hourly precipitation extremes in a changing climate M. Lennartz et al. https://doi.org/10.5194/nhess-26-3273-2026
- Evaluation and projection of extreme rainfall from a large ensemble of high–resolution regional climate models in Australia L. Jayaweera et al. https://doi.org/10.1016/j.wace.2025.100818
- Projected changes in sub-daily extreme precipitation: comparing temperature-scaling approaches and convection-permitting models across an Alpine gradient R. Akbary et al. https://doi.org/10.1016/j.jhydrol.2026.135356
- A Universal Multifractals perspective into the link between rainfall variability and temperature A. Gires & Y. Torres https://doi.org/10.5194/hess-30-4741-2026
- A non-stationary climate-informed weather generator for assessing future flood risks V. Nguyen et al. https://doi.org/10.5194/ascmo-10-195-2024
- Meta-modelling paths of simple climate models using neural networks and dirichlet polynomials: an application to DICE E. Gobet et al. https://doi.org/10.1007/s13385-025-00438-3
- Impacts of urban dynamics and thermodynamics on convective rainfall across different urban forms H. Torelló-Sentelles et al. https://doi.org/10.1016/j.uclim.2025.102499
- Non-stationary frequency analysis of long-term convection permitting simulations reveals sub-daily extreme precipitation changes in central-southern Europe M. Lompi et al. https://doi.org/10.1016/j.advwatres.2025.105071
- Future precipitation extremes and urban flood risk assessment using a non-stationary and convection-permitting climate-hydrodynamic modeling framework P. Laux et al. https://doi.org/10.1016/j.jhydrol.2025.133607
- Hotter summers, heavier showers: Global warming and its impact on Swiss short-duration rainfall extremes N. Peleg et al. https://doi.org/10.1016/j.wace.2025.100829
- Topographic and Climatic Controls on Depth–Duration–Frequency Curve Parameters in the Gargano Promontory (Southern Italy) G. Iemmolo et al. https://doi.org/10.3390/hydrology13070188
Saved (final revised paper)
Latest update: 06 Aug 2026
Short summary
We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
We present a new physical-based method for estimating extreme sub-hourly precipitation return...
