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<p>This study examines the associations between regional precipitation patterns in Bangladesh and major global climate drivers, namely the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), within a statistical framework. The Intentionally Biased Bootstrapping (IBB) method is applied to generate extreme precipitation indices—seasonal total precipitation (PRCPTOT), one-day maximum precipitation (Rx1day), and heavy precipitation days (R10mm)—by systematically perturbing the sea surface temperatures (SST) of ENSO, IOD, and combined ENSO–IOD indices. The findings demonstrate that the IBB method reliably captures precipitation responses across seasons. During the pre-monsoon period, simulated cold ENSO events induce widespread negative PRCPTOT anomalies, while warm ENSO phases enhance rainfall in the southwestern zone. In the monsoon season, synthetic warm ENSO events generally increase PRCPTOT across most stations, whereas cold IOD events are associated with widespread deficits. Post-monsoon precipitation emerges as most sensitive, with strong negative anomalies under warm ENSO conditions, particularly in central and southern regions, and positive anomalies under cold IOD phases. Comparative assessments reveal that ENSO exerts a stronger overall influence than IOD, although combined ENSO–IOD simulations largely reproduce ENSO-driven anomaly patterns while introducing localized variations—most notably positive PRCPTOT anomalies in northern Bangladesh under warm ENSO–IOD phases. The Rx1day and R10mm indices show consistent responses to simulated ENSO, IOD, and ENSO–IOD anomalies, reinforcing the robustness of precipitation sensitivity to these climate drivers. Pearson correlation analyses further validate the results, confirming that the direction of simulated anomalies aligns with observed linear relationships between precipitation indices and ENSO/IOD. Overall, the results highlight that precipitation in Bangladesh is most sensitive during the post-monsoon season, with ENSO as the dominant driver, while ENSO–IOD interactions provide additional regional modulation. These findings offer valuable insights for water resource management and can inform adaptive strategies to address precipitation variability linked to global climate oscillations.</p>

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Evaluating climate driver impacts on precipitation extremes through a bootstrapping-based statistical framework in Bangladesh

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Shabbir Ahmed Osmani,
  • Changhyun Jun,
  • Jongjin Baik,
  • Jong-Suk Kim,
  • Abdullah Md. Omar

摘要

This study examines the associations between regional precipitation patterns in Bangladesh and major global climate drivers, namely the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), within a statistical framework. The Intentionally Biased Bootstrapping (IBB) method is applied to generate extreme precipitation indices—seasonal total precipitation (PRCPTOT), one-day maximum precipitation (Rx1day), and heavy precipitation days (R10mm)—by systematically perturbing the sea surface temperatures (SST) of ENSO, IOD, and combined ENSO–IOD indices. The findings demonstrate that the IBB method reliably captures precipitation responses across seasons. During the pre-monsoon period, simulated cold ENSO events induce widespread negative PRCPTOT anomalies, while warm ENSO phases enhance rainfall in the southwestern zone. In the monsoon season, synthetic warm ENSO events generally increase PRCPTOT across most stations, whereas cold IOD events are associated with widespread deficits. Post-monsoon precipitation emerges as most sensitive, with strong negative anomalies under warm ENSO conditions, particularly in central and southern regions, and positive anomalies under cold IOD phases. Comparative assessments reveal that ENSO exerts a stronger overall influence than IOD, although combined ENSO–IOD simulations largely reproduce ENSO-driven anomaly patterns while introducing localized variations—most notably positive PRCPTOT anomalies in northern Bangladesh under warm ENSO–IOD phases. The Rx1day and R10mm indices show consistent responses to simulated ENSO, IOD, and ENSO–IOD anomalies, reinforcing the robustness of precipitation sensitivity to these climate drivers. Pearson correlation analyses further validate the results, confirming that the direction of simulated anomalies aligns with observed linear relationships between precipitation indices and ENSO/IOD. Overall, the results highlight that precipitation in Bangladesh is most sensitive during the post-monsoon season, with ENSO as the dominant driver, while ENSO–IOD interactions provide additional regional modulation. These findings offer valuable insights for water resource management and can inform adaptive strategies to address precipitation variability linked to global climate oscillations.