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<p>This study examines the influence of large-scale climatic phenomena—sunspot activity (SSN) and the El Niño-Southern oscillation (ENSO), represented by the Southern oscillation index (SOI) on precipitation, temperature, and dew point patterns in Gilan province, Iran. Using wavelet signal analysis, including continuous wavelet transforms (CWT), cross-wavelet transforms (XWT), wavelet coherence (WTC), and time-lag correlation analysis (TLCA), the research investigates temporal and frequency-domain relationships between these factors and regional climatic variables. A 73-year dataset (1951–2023) reveals cyclical patterns in SSN and SOI, correlating with significant 10-year periodicities in precipitation trends. The study further reveals ENSO and SSN’s influence on long-term temperature and dew point fluctuations. While sunspot activity and ENSO both modulate climate variability, the study’s scope is limited to Gilan province and excludes other atmospheric drivers or anthropogenic influences, which may also affect local hydrology. The findings suggest integrating solar and climatic indices into predictive models could improve long-term climate forecasts, supporting water resource management in this climate-sensitive region. This research provides novel insights for policymakers to address water-related challenges amid climatic variability by applying advanced wavelet methods to analyze multi-decadal interactions.</p>

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Analyzing the cyclical impact of sunspot activity and ENSO on precipitation patterns in Gilan province: a wavelet-based approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Mahboubeh Molavi-Arabshahi,
  • Sara Eskandari

摘要

This study examines the influence of large-scale climatic phenomena—sunspot activity (SSN) and the El Niño-Southern oscillation (ENSO), represented by the Southern oscillation index (SOI) on precipitation, temperature, and dew point patterns in Gilan province, Iran. Using wavelet signal analysis, including continuous wavelet transforms (CWT), cross-wavelet transforms (XWT), wavelet coherence (WTC), and time-lag correlation analysis (TLCA), the research investigates temporal and frequency-domain relationships between these factors and regional climatic variables. A 73-year dataset (1951–2023) reveals cyclical patterns in SSN and SOI, correlating with significant 10-year periodicities in precipitation trends. The study further reveals ENSO and SSN’s influence on long-term temperature and dew point fluctuations. While sunspot activity and ENSO both modulate climate variability, the study’s scope is limited to Gilan province and excludes other atmospheric drivers or anthropogenic influences, which may also affect local hydrology. The findings suggest integrating solar and climatic indices into predictive models could improve long-term climate forecasts, supporting water resource management in this climate-sensitive region. This research provides novel insights for policymakers to address water-related challenges amid climatic variability by applying advanced wavelet methods to analyze multi-decadal interactions.