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<p>Understanding long-term rainfall patterns is essential for Pakistan. Data gaps and shifting climate patterns in Pakistan have necessitated the study of rainfall behaviour using satellite- and gauge-based land-surface models. This study employed gridded datasets from the climate research unit (CRU), ERA5, and WorldClim for 1950 to 2024 to assess spatiotemporal precipitation variability. The space–time cube framework was used to evaluate precipitation change using the Mann–Kendall trend statistic, Sen’s slope, and Getis-Ord Gi* in an emerging hot spot analysis. All the results showed spatial heterogeneity across Pakistan. An increasing precipitation trend was observed over the Lower Himalayas and central-eastern Punjab, while Balochistan and southern Sindh showed declining trends. A persistent and intensifying hotspot dominated the Himalayan foothills. ERA5 showed a minor but still significant increase in precipitation, with a zone of 3087&#xa0;km² (z = 2.71 and <i>p</i> = 0.01). The 95% confidence category in ERA5 covered the largest area of increasing precipitation, 42,567&#xa0;km², while CRU indicated a comparatively restricted area of 5675&#xa0;km². At the same time, the upper Punjab showed a fluctuating hotspot, and a diminishing coldspot cluster was observed across the arid central and south Punjab and eastern Balochistan regions. Despite differences in resolution, all datasets converged, confirming the robustness of the observed spatial patterns. The study compared the strengthened rainfall regime in Punjab with enhanced moisture transport, increased aerosol levels, and thermodynamic intensification of the South Asian monsoon under a warming climate. Rising rainfall trends in densely populated agricultural regions highlight the vulnerability to floods and waterlogging. These findings emphasize the need for an accurate early warning system and for revising cropping calendars and irrigation strategies across Pakistan’s agricultural belts.</p>

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Assessing seven-decade precipitation patterns using CRU, ERA5, and WorldClim with space–time cube trend detection

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Hafsah Batool,
  • Amir Ali,
  • Ameer Faisal,
  • Sumaira Kousar,
  • Syed Amer Mahmood,
  • Hamid Gulzar,
  • Nazih Y. Rebouh,
  • Khadeijah Yahya Faqeih,
  • Somayah Moshrif Alamri,
  • Eman Rafi Alamery,
  • Aqil Tariq

摘要

Understanding long-term rainfall patterns is essential for Pakistan. Data gaps and shifting climate patterns in Pakistan have necessitated the study of rainfall behaviour using satellite- and gauge-based land-surface models. This study employed gridded datasets from the climate research unit (CRU), ERA5, and WorldClim for 1950 to 2024 to assess spatiotemporal precipitation variability. The space–time cube framework was used to evaluate precipitation change using the Mann–Kendall trend statistic, Sen’s slope, and Getis-Ord Gi* in an emerging hot spot analysis. All the results showed spatial heterogeneity across Pakistan. An increasing precipitation trend was observed over the Lower Himalayas and central-eastern Punjab, while Balochistan and southern Sindh showed declining trends. A persistent and intensifying hotspot dominated the Himalayan foothills. ERA5 showed a minor but still significant increase in precipitation, with a zone of 3087 km² (z = 2.71 and p = 0.01). The 95% confidence category in ERA5 covered the largest area of increasing precipitation, 42,567 km², while CRU indicated a comparatively restricted area of 5675 km². At the same time, the upper Punjab showed a fluctuating hotspot, and a diminishing coldspot cluster was observed across the arid central and south Punjab and eastern Balochistan regions. Despite differences in resolution, all datasets converged, confirming the robustness of the observed spatial patterns. The study compared the strengthened rainfall regime in Punjab with enhanced moisture transport, increased aerosol levels, and thermodynamic intensification of the South Asian monsoon under a warming climate. Rising rainfall trends in densely populated agricultural regions highlight the vulnerability to floods and waterlogging. These findings emphasize the need for an accurate early warning system and for revising cropping calendars and irrigation strategies across Pakistan’s agricultural belts.