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<front>
<journal-meta>
<journal-id journal-id-type="publisher">EGUsphere</journal-id>
<journal-title-group>
<journal-title>EGUsphere</journal-title>
<abbrev-journal-title abbrev-type="publisher">EGUsphere</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">EGUsphere</abbrev-journal-title>
</journal-title-group>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/egusphere-2026-6563</article-id>
<title-group>
<article-title>Antecedent rainfall patterns and situational vulnerability of victims of rainfall-induced fatal landslides in Pakistan</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Bai</surname>
<given-names>Shibiao</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhu</surname>
<given-names>Wenxin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Wang</surname>
<given-names>Xiu</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhou</surname>
<given-names>Pinggen</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>School of Marine Science and Engineering, State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>China Institute of Geo-Environment Monitoring, Beijing, China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>05</day>
<month>11</month>
<year>2026</year>
</pub-date>
<volume>2026</volume>
<fpage>1</fpage>
<lpage>25</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Shibiao Bai et al.</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2026/egusphere-2026-6563/">This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-6563/</self-uri>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2026/egusphere-2026-6563/egusphere-2026-6563.pdf">The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-6563/egusphere-2026-6563.pdf</self-uri>
<abstract>
<p>Fatalities from rainfall-induced landslides depend not only on rainfall conditions but also on the time of occurrence, the exposure setting and demographic vulnerability. In data-scarce mountain regions such as Pakistan, event-scale rainfall histories and individual casualty records have long been disconnected. This study places event-scale rainfall and individual victim circumstances within a single event catalogue, characterises antecedent rainfall patterns and tests whether they explain fatality magnitude, in order to inform scenario-based early warning in data-scarce mountains. Based on a 2004&amp;ndash;2024 catalogue of rainfall-induced fatal landslides in Pakistan (positioned as a lower-bound estimate because flood&amp;ndash;landslide&amp;ndash;debris-flow&amp;ndash;building-collapse cascades mask landslide-attributed deaths), 213 of 250 events with spatial accuracy levels I&amp;ndash;II were matched to CHIRPS 0.05&amp;deg; daily rainfall, and individual attributes of 309 fatalities during 2018&amp;ndash;2024 were compiled. Results show that 53.5 % of events have zero event-day rainfall in CHIRPS; among four antecedent-rainfall types defined by event-day and 7-day antecedent rainfall, the low-event-day-rainfall types C and D together account for 86.4 %, and sensitivity tests using 15/40, 20/50 and 25/60 mm boundaries give 81.2&amp;ndash;89.7 %, indicating robustness to the classification boundaries. The intensity&amp;ndash;duration quantile relations share a common exponent of &amp;minus;0.59, yet no rainfall metric correlates significantly with per-event fatality. Victim records show that 66.0 % of deaths with known setting occurred indoors and 74.5 % of those with known timing occurred at night, making the night&amp;ndash;indoor combination the most concentrated exposure; both gender and age are significantly associated with exposure setting (&amp;chi;&amp;sup2; = 28.61, Cram&amp;eacute;r&apos;s V = 0.367; &amp;chi;&amp;sup2; = 35.34, V = 0.362; p &amp;lt; 0.001), and the setting&amp;ndash;timing association is also significant (&amp;chi;&amp;sup2; = 70.87, df = 2, p &amp;lt; 0.001, V = 0.594). Rainfall therefore characterises the hydro-meteorological background of slope instability, whereas fatality consequences additionally depend on timing, exposed space and demographic attributes. Early-warning systems should consider both short-duration and antecedent cumulative rainfall, and translate warnings into scenario-based actions such as night-time evacuation of dwellings, mountain road control and management of children&apos;s activity spaces.</p>
</abstract>
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<funding-group>
<award-group id="gs1">
<funding-source>National Key Research and Development Program of China</funding-source>
<award-id>2023YFC3007200</award-id>
</award-group>
</funding-group>
</article-meta>
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