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Weather-Regime-Based Heatwave Risk Typing and Urban Climate Resilience Assessment in New Delhi (1997–2016)

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Abstract

Extreme heat across the North Indian Plain has intensified in recent decades, with the temperature in Delhi repeatedly exceeding 48 °C. We present a physically interpretable and computationally efficient typology of heatwave risk using aggregated station observations of daily mean temperature, relative humidity, wind speed, and pressure from 1997 to 2016. Quality-controlled, standardized daily features (PCA-verified) were clustered with k-means; internal validity indices (Silhouette, Calinski–Harabasz, and Davies–Bouldin) identified an optimal partition with k = 3, defining three distinct weather regimes. Coupling these regimes with an absolute heatwave criterion (daily mean ≥30 °C for ≥3 days) revealed a pronounced gradient: a dry–hot, high-pressure regime (41% of days) accounted for 63% of heatwave days (mean 33.4 °C; median duration ≈17 days); a mild–humid background (59%) yielded ~8% incidence; and a rare blocking-driven dry intrusion (<1%) produced heatwaves each time, with mean temperatures of >35 °C and episodes persisting for ≥30 days. Regime–heatwave relationships were statistically significant and robust across sensitivity tests, including variations in k, alternative clustering algorithms, and bootstrap resampling. This four-stage workflow consists of data preparation, feature extraction, regime classification, and heatwave risk attribution and provides a transparent basis for regime-aware early warning, demand-side energy management, and public health protection in Delhi and is transferable to other rapidly urbanizing regions.
Original languageEnglish
Article number1179
JournalAtmosphere
Volume16
Issue number10
DOIs
Publication statusPublished - 13 Oct 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • K-means clustering
  • New Delhi, India
  • heatwave
  • risk classification
  • urban resilience
  • weather regime

ASJC Scopus subject areas

  • Environmental Science (miscellaneous)
  • Atmospheric Science

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