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<p>In the age of global warming, sub-daily precipitation extremes may become more severe in response to temperature changes more quickly than daily precipitation extremes, presenting growing threats to human society and the natural ecosystem. To guide climate adaptation and inform future precipitation projections, a systematic study of the spatiotemporal and climatology changes in daily precipitation extremes is necessary. Based on the daily precipitation data of 108 meteorological stations, we used a recently developed set of sub-daily extreme precipitation indices to analyze the characteristics and trends of daily precipitation extremes across four provinces of Pakistan during the monsoon seasons from 1981 to 2022. When comparing 1981–999 with 2000–2022, statistically significant extreme increase has been found in Punjab and KPK: Rx1day increased by 7–15% (36.9–39.2&#xa0;mm in Punjab; 34.7–40.1&#xa0;mm in KPK), and R95p increased by 30% (130.6–166.9&#xa0;mm in Punjab; 128.4–177.8&#xa0;mm in KPK). The frequency indices indicate that there is an increase in heavy rainfall days (R10mm) by 6–9 in Punjab and 7–10 in KPK, but Balouchistan and Sindh have weaker changes with a prolonged dry period (CDD &gt; 45&#xa0;days). Total seasonal precipitation (PRCPTOT) increased by 30–45% (233.30–303.33&#xa0;mm in Punjab and 231.69–336.89&#xa0;mm in KPK), while SDII also increased, displaying higher mean precipitation per wet day. We used ordinary kriging (OK) and universal kriging (UK) methods, selected based on performance metrics such as RMSE, to interpolate spatial patterns of precipitation indices across four provinces of Pakistan. The RMSE values showed that for Rx1day, the Gaussian model had the lowest errors (OK = 0.0603, UK = 0.0573), while for Rx3day, Circular (OK = 0.7969) and Gaussian (UK = 0.7134) performed best. For other indices, K-Bessel minimized RMSE for R95p and R99p (OK = 0.1140, 0.3157; UK = 0.0230, 0.3519), threshold indices R10mm and R20mm were stable (OK = 0.9294–0.9575, 0.4375–0.4517; UK = 0.9328–0.9617, 0.4375–0.4528), and SDII, CWD, and PRCPTOT showed slightly lower RMSE in UK compared to OK. The OK and UK Kriging of spatial interpolation reveal a southwest northeast gradient where flood prone areas are in the eastern part of Punjab, north of KPK and coastal Sindh whereas areas with persistent drought susceptibility exist in Balouchistan. Field significance testing also shows strong positive changes in over 70% of indices in 2000–2022, with a regime shift towards wetter extremes. These findings represent new indications of increasing extremes precipitation at daily frequencies in Pakistan, which is essential in informing provincial-level flood risks, drought preparedness, as well as climate accommodation policy. Our results provide complementary evidence for change in extreme precipitation at daily timescale over Pakistan and could contribute to social decision-making for climate adaptation.</p>

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Assessment of Climatological Trends, Spatial Distribution and Driving Forces of Extreme Daily Precipitation Events

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Habib Ullah,
  • Rizwan Niaz,
  • Ijaz Hussain,
  • Huda M. Alshanbari,
  • Mohammed M. A. Almazah,
  • Hefa Cheng

摘要

In the age of global warming, sub-daily precipitation extremes may become more severe in response to temperature changes more quickly than daily precipitation extremes, presenting growing threats to human society and the natural ecosystem. To guide climate adaptation and inform future precipitation projections, a systematic study of the spatiotemporal and climatology changes in daily precipitation extremes is necessary. Based on the daily precipitation data of 108 meteorological stations, we used a recently developed set of sub-daily extreme precipitation indices to analyze the characteristics and trends of daily precipitation extremes across four provinces of Pakistan during the monsoon seasons from 1981 to 2022. When comparing 1981–999 with 2000–2022, statistically significant extreme increase has been found in Punjab and KPK: Rx1day increased by 7–15% (36.9–39.2 mm in Punjab; 34.7–40.1 mm in KPK), and R95p increased by 30% (130.6–166.9 mm in Punjab; 128.4–177.8 mm in KPK). The frequency indices indicate that there is an increase in heavy rainfall days (R10mm) by 6–9 in Punjab and 7–10 in KPK, but Balouchistan and Sindh have weaker changes with a prolonged dry period (CDD > 45 days). Total seasonal precipitation (PRCPTOT) increased by 30–45% (233.30–303.33 mm in Punjab and 231.69–336.89 mm in KPK), while SDII also increased, displaying higher mean precipitation per wet day. We used ordinary kriging (OK) and universal kriging (UK) methods, selected based on performance metrics such as RMSE, to interpolate spatial patterns of precipitation indices across four provinces of Pakistan. The RMSE values showed that for Rx1day, the Gaussian model had the lowest errors (OK = 0.0603, UK = 0.0573), while for Rx3day, Circular (OK = 0.7969) and Gaussian (UK = 0.7134) performed best. For other indices, K-Bessel minimized RMSE for R95p and R99p (OK = 0.1140, 0.3157; UK = 0.0230, 0.3519), threshold indices R10mm and R20mm were stable (OK = 0.9294–0.9575, 0.4375–0.4517; UK = 0.9328–0.9617, 0.4375–0.4528), and SDII, CWD, and PRCPTOT showed slightly lower RMSE in UK compared to OK. The OK and UK Kriging of spatial interpolation reveal a southwest northeast gradient where flood prone areas are in the eastern part of Punjab, north of KPK and coastal Sindh whereas areas with persistent drought susceptibility exist in Balouchistan. Field significance testing also shows strong positive changes in over 70% of indices in 2000–2022, with a regime shift towards wetter extremes. These findings represent new indications of increasing extremes precipitation at daily frequencies in Pakistan, which is essential in informing provincial-level flood risks, drought preparedness, as well as climate accommodation policy. Our results provide complementary evidence for change in extreme precipitation at daily timescale over Pakistan and could contribute to social decision-making for climate adaptation.