---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Mengzhu
      foaf_name: Chen, Mengzhu
      foaf_surname: Chen
  - foaf_Person:
      foaf_givenName: Shanti Shwarup
      foaf_name: Mahto, Shanti Shwarup
      foaf_surname: Mahto
  - foaf_Person:
      foaf_givenName: Xiaogang
      foaf_name: He, Xiaogang
      foaf_surname: He
  - foaf_Person:
      foaf_givenName: Changhyun
      foaf_name: Jun, Changhyun
      foaf_surname: Jun
  - foaf_Person:
      foaf_givenName: Athanasios
      foaf_name: Paschalis, Athanasios
      foaf_surname: Paschalis
  - foaf_Person:
      foaf_givenName: Nadav
      foaf_name: Peleg, Nadav
      foaf_surname: Peleg
  - foaf_Person:
      foaf_givenName: Giuseppe
      foaf_name: Mascaro, Giuseppe
      foaf_surname: Mascaro
  - foaf_Person:
      foaf_givenName: Simone
      foaf_name: Fatichi, Simone
      foaf_surname: Fatichi
      foaf_workInfoHomepage: http://www.librecat.org/personId=cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  bibo_doi: 10.1038/s44304-025-00148-6
  bibo_volume: 2
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2948-2100
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_title: 'Record-breaking rainfall: a stochastic approach for its prediction@'
...