---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '22586'
abstract:
- lang: eng
  text: Extreme rainfall events that break previous records are occurring more frequently
    worldwide, leading to severe flooding and infrastructure damage. Conventional
    flood design approaches, based on extreme value analysis (EVA) of limited historical
    data, often fail to anticipate such unprecedented extremes. Here, we present a
    stochastic approach that leverages the Advanced Weather Generator (AWE-GEN) to
    simulate a large ensemble of 100-year hourly rainfall time series, explicitly
    accounting for internal climate variability. By excluding the record-breaking
    event year during calibration, we assess the ability of our proposed method to
    reproduce unseen record-breaking events. We evaluated this approach using data
    from 2703 rain stations across nine countries. Our results show that the stochastic
    approach captures record-breaking events more reliably than EVA, achieving success
    rates exceeding 85% for 3–12-hour durations at a 100-year return period threshold.
    This framework provides a more robust way for estimating rainfall extremes and
    supports the design of resilient infrastructure under deep uncertainty.
article_number: '98'
article_processing_charge: No
article_type: original
author:
- first_name: Mengzhu
  full_name: Chen, Mengzhu
  last_name: Chen
- first_name: Shanti Shwarup
  full_name: Mahto, Shanti Shwarup
  last_name: Mahto
- first_name: Xiaogang
  full_name: He, Xiaogang
  last_name: He
- first_name: Changhyun
  full_name: Jun, Changhyun
  last_name: Jun
- first_name: Athanasios
  full_name: Paschalis, Athanasios
  last_name: Paschalis
- first_name: Nadav
  full_name: Peleg, Nadav
  last_name: Peleg
- first_name: Giuseppe
  full_name: Mascaro, Giuseppe
  last_name: Mascaro
- first_name: Simone
  full_name: Fatichi, Simone
  id: cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  last_name: Fatichi
citation:
  ama: 'Chen M, Mahto SS, He X, et al. Record-breaking rainfall: a stochastic approach
    for its prediction. <i>npj Natural Hazards</i>. 2025;2. doi:<a href="https://doi.org/10.1038/s44304-025-00148-6">10.1038/s44304-025-00148-6</a>'
  apa: 'Chen, M., Mahto, S. S., He, X., Jun, C., Paschalis, A., Peleg, N., … Fatichi,
    S. (2025). Record-breaking rainfall: a stochastic approach for its prediction.
    <i>Npj Natural Hazards</i>. Springer Nature. <a href="https://doi.org/10.1038/s44304-025-00148-6">https://doi.org/10.1038/s44304-025-00148-6</a>'
  chicago: 'Chen, Mengzhu, Shanti Shwarup Mahto, Xiaogang He, Changhyun Jun, Athanasios
    Paschalis, Nadav Peleg, Giuseppe Mascaro, and Simone Fatichi. “Record-Breaking
    Rainfall: A Stochastic Approach for Its Prediction.” <i>Npj Natural Hazards</i>.
    Springer Nature, 2025. <a href="https://doi.org/10.1038/s44304-025-00148-6">https://doi.org/10.1038/s44304-025-00148-6</a>.'
  ieee: 'M. Chen <i>et al.</i>, “Record-breaking rainfall: a stochastic approach for
    its prediction,” <i>npj Natural Hazards</i>, vol. 2. Springer Nature, 2025.'
  ista: 'Chen M, Mahto SS, He X, Jun C, Paschalis A, Peleg N, Mascaro G, Fatichi S.
    2025. Record-breaking rainfall: a stochastic approach for its prediction. npj
    Natural Hazards. 2, 98.'
  mla: 'Chen, Mengzhu, et al. “Record-Breaking Rainfall: A Stochastic Approach for
    Its Prediction.” <i>Npj Natural Hazards</i>, vol. 2, 98, Springer Nature, 2025,
    doi:<a href="https://doi.org/10.1038/s44304-025-00148-6">10.1038/s44304-025-00148-6</a>.'
  short: M. Chen, S.S. Mahto, X. He, C. Jun, A. Paschalis, N. Peleg, G. Mascaro, S.
    Fatichi, Npj Natural Hazards 2 (2025).
das_tickbox: '1'
date_created: 2026-07-27T12:30:25Z
date_published: 2025-10-29T00:00:00Z
date_updated: 2026-08-12T08:59:35Z
day: '29'
doi: 10.1038/s44304-025-00148-6
extern: '1'
intvolume: '         2'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1038/s44304-025-00148-6
month: '10'
oa: 1
oa_version: Published Version
publication: npj Natural Hazards
publication_identifier:
  eissn:
  - 2948-2100
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
status: public
title: 'Record-breaking rainfall: a stochastic approach for its prediction'
tmp:
  image: /images/cc_by_nc_nd.png
  legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
  name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
    (CC BY-NC-ND 4.0)
  short: CC BY-NC-ND (4.0)
type: journal_article
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 2
year: '2025'
...