{
  "abstract": "Long records of rainfall contain critical information about the temporal organization of precipitation within a year, including wet and dry spells. Detecting when this temporal pattern changes is central to climate-impact assessment and environmental monitoring.A common data structure features observations from m locations over T years. For location i and year t, the record is a vector of daily measurements $X_{i,t} = (X_{i,t,1}, ..., X_{i,t,D})^\\top$, with D = 365. The goal is to identify an unknown year $t_0$ when the temporal rainfall pattern changes across the region. Because changes in the intra-annual pattern affect the frequency-domain behavior of the daily sequence, we represent each yearly curve through a spectral feature capturing the strength of temporal oscillation at a fixed frequency.Classical changepoint methods often rely on parametric likelihoods or Gaussian approximations, which are hard to justify for skewed and heavy-tailed rainfall data. Furthermore, many procedures only return a point estimate. A confidence set for the change year is crucial in environmental applications to quantify temporal uncertainty and distinguish sharp transitions from weak evidence.This paper develops a distribution-free framework for detecting a synchronized structural change in yearly rainfall patterns across independently monitored locations. For each location-year pair, we compute a fixed-frequency nonparametric spectral density estimate. These features form a $T \\times m$ data matrix. By exploiting the exchangeability of yearly spectral features before and after the true changepoint, alongside spatial independence, we construct finite-sample valid confidence sets for the unknown change year.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13594",
  "author_total": 2,
  "authors": [
    {
      "name": "Aaditya Jain"
    },
    {
      "name": "Abhishek Bhattacharjee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4kwv56nd5p791.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13594",
  "fulltext_url": null,
  "id": "4kwv56nd5p791",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13594",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1804|Advanced Statistical Process Monitoring"
    ]
  ],
  "openalex": "W7213364809",
  "pdf_url": "https://arxiv.org/pdf/2609.13594",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4kwv56nd5p791.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:36Z",
      "native_id": "2609.13594",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13594"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Distribution-Free Changepoint Inference for Rainfall Patterns",
  "title_text": "Distribution-Free Changepoint Inference for Rainfall Patterns",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:36Z",
  "url": "https://research.draconitic.com/paper/4kwv56nd5p791",
  "venue": "arXiv",
  "versions": []
}