Batool et al. (2026) Assessing seven-decade precipitation patterns using CRU, ERA5, and WorldClim with space–time cube trend detection

Identification

• Journal: Climate Dynamics
• Year: 2026
• Date: 2026-04-01
• Authors: 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
• DOI: 10.1007/s00382-026-08168-2

Research Groups

• Institute of Space Science, University of the Punjab, Lahore, Pakistan
• Institute of Geography, University of the Punjab, Lahore, Pakistan
• Institute of Environmental Engineering, RUDN University, Moscow, Russia
• Department of Geography and Environmental Sustainability, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
• Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, Mississippi State, MS, USA

Short Summary

This study assessed seven-decade spatiotemporal precipitation patterns across Pakistan using CRU, ERA5, and WorldClim datasets with a space–time cube framework. It revealed a distinct north-south precipitation polarity, with increasing trends and intensifying hotspots in the northern and northeastern regions, contrasting with decreasing trends and persistent coldspots in the arid southwest, and identified a transitional diminishing coldspot zone in central Pakistan.

Objective

• Identify regions experiencing statistically significant increasing or decreasing precipitation trends across Pakistan.
• Detect persistent, intensifying, diminishing, and shifting hotspot and coldspot patterns of precipitation.
• Examine how precipitation regimes have evolved across Pakistan’s major climatic zones since 1950.

Study Configuration

• Spatial Scale: Pakistan (between 24° and 37°N and 60° and 77°E), covering diverse landscapes from coastal plains to mountainous terrain. Datasets used had resolutions of 0.5° (~55 km), 0.1° (~9 km), and 0.083° (~9 km).
• Temporal Scale: 1950 to 2024 (75 years), using monthly precipitation data.

Methodology and Data

• Models used: Space–Time Cube (STC) framework, Mann–Kendall (MK) trend statistic, Sen’s slope estimator, Getis-Ord Gi* statistic, Emerging Hot Spot Analysis (EHSA).
• Data sources: Gridded datasets: Climate Research Unit (CRU) version TS 4.09, ERA5-Land reanalysis monthly dataset, and WorldClim v2.1 downscaled climatology.

Main Results

• Spatial Heterogeneity: All analyses consistently showed significant spatial heterogeneity in precipitation patterns across Pakistan.
• Increasing Trends and Hotspots:
  • The Lower Himalayas and central-eastern Punjab exhibited significant increasing precipitation trends and were dominated by persistent and intensifying hotspots.
  • CRU and WorldClim showed strong increasing signals at the 99% confidence level (z = 3.96 and 3.28, p < 0.01) over 9263 km² and 9527 km², respectively.
  • ERA5 indicated a significant increase over 3087 km² (z = 2.71, p = 0.01), with its 95% confidence category covering the largest area of increasing precipitation (42,567 km²).
  • Lower Himalayas showed strong positive trends in CRU (τ = 0.16, p = 0.039, slope = 2.24 mm/year) and WorldClim (τ = 0.15, p = 0.057, slope = 2.04 mm/year), primarily during the monsoon season (June–August).
  • South Punjab showed consistent, positive, and significant trends in CRU (τ = 0.22, p = 0.005, slope = 0.74 mm/year) and WorldClim (τ = 0.21, p = 0.009, slope = 0.78 mm/year).
• Decreasing Trends and Coldspots:
  • Southwestern Balochistan and southern Sindh showed consistent decreasing precipitation trends, forming extensive intensifying and persistent coldspot clusters (158,000 to 173,000 km²).
  • At the 99% confidence level (p = 0.01), ERA5 recorded a z-score of −2.71 over 3087 km², CRU showed −3.96 across 9263 km², and WorldClim exhibited −3.28 over 9527 km².
  • Southwestern Balochistan generally showed negative slopes, particularly in CRU (τ = −0.15, p = 0.055, slope = −0.45 mm/year).
• Transitional Zone: A diminishing coldspot cluster was observed across arid central and south Punjab and eastern Balochistan regions (WorldClim: 267,083 km², ERA5: 222,470 km², CRU: 201,102 km²), indicating a gradual weakening of historically arid conditions.
• Dataset Agreement: Despite differences in resolution and data assimilation, all three datasets (CRU, ERA5, WorldClim) converged on the same spatial patterns and directional trends, reinforcing the robustness of the findings.
• Societal Implications: Rising rainfall in densely populated agricultural regions increases vulnerability to floods and waterlogging, necessitating improved early warning systems and revised agricultural strategies.

Contributions

• Provides the first multi-dataset, space–time assessment of Pakistan’s long-term precipitation evolution over a 75-year record using an integrated analytical framework combining Mann–Kendall trend, Sen’s slope, and emerging hotspot diagnostics within a Space–Time Cube (STC).
• Introduces a novel approach by embedding multiple gridded datasets within a unified STC framework to jointly quantify monotonic trends and the spatiotemporal evolution of precipitation hotspots and coldspots.
• Moves beyond conventional trend-only analyses by revealing the persistence, intensification, and shifting behavior of rainfall regimes across Pakistan’s major climatic zones.
• Offers actionable insights for flood risk management, climate-resilient agrarian planning, and long-term adaptation strategies, demonstrating the value of integrating spatiotemporal analytics into national climate risk assessments.

Funding

• Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R674), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Citation

@article{Batool2026Assessing,
  author = {Batool, Hafsah and Ali, Amir and Faisal, Ameer and Kousar, Sumaira and Mahmood, Syed Amer and Gulzar, Hamid and Rebouh, Nazih Y. and Faqeih, Khadeijah Yahya and Alamri, Somayah Moshrif and Alamery, Eman Rafi and Tariq, Aqil},
  title = {Assessing seven-decade precipitation patterns using CRU, ERA5, and WorldClim with space–time cube trend detection},
  journal = {Climate Dynamics},
  year = {2026},
  doi = {10.1007/s00382-026-08168-2},
  url = {https://doi.org/10.1007/s00382-026-08168-2}
}