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<p>Understanding the skill of multi-satellite precipitation estimates and the physical mechanisms driving rainfall extremes is critical for hydroclimatic risk management in data-sparse, semi-arid regions. This study presents a dual assessment over a topographically complex mountain-desert transition zone in Isfahan province, Iran. First, we conduct a rigorous 20-year validation of four leading multi-satellite products (IMERG, GSMaP, CHIRPS, and PERSIANN − CDR) against 85 rain gauges. Second, we diagnose the large-scale atmospheric architecture of extreme wet months using ERA5 reanalysis. The validation reveals the clear superiority of GPM-era products, with GSMaP and IMERG exhibiting the highest daily correlation with ground observations (median CC of 0.58 and 0.54, respectively) and the most balanced performance. Conversely, CHIRPS shows a persistent wet bias, while PERSIANN − CDR is found to be unreliable for daily applications. Crucially, the synoptic analysis identifies a recurrent atmospheric pattern strongly associated with extreme wet events: a deep upper-level trough at 200&#xa0;hPa over the Eastern Mediterranean induces a low-level cyclonic anomaly that drives a robust southwesterly moisture transport from the Red Sea and Persian Gulf. By linking statistical product skill to physical atmospheric drivers, this research provides actionable intelligence for water resource management and establishes a physical basis for improving regional climate predictability.</p>

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Linking the skill of multi-satellite precipitation estimates to the synoptic drivers of extreme events across a mountain-desert transition zone

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Moein Tosan,
  • Vahid Nourani,
  • Selin Uzelaltinbulat

摘要

Understanding the skill of multi-satellite precipitation estimates and the physical mechanisms driving rainfall extremes is critical for hydroclimatic risk management in data-sparse, semi-arid regions. This study presents a dual assessment over a topographically complex mountain-desert transition zone in Isfahan province, Iran. First, we conduct a rigorous 20-year validation of four leading multi-satellite products (IMERG, GSMaP, CHIRPS, and PERSIANN − CDR) against 85 rain gauges. Second, we diagnose the large-scale atmospheric architecture of extreme wet months using ERA5 reanalysis. The validation reveals the clear superiority of GPM-era products, with GSMaP and IMERG exhibiting the highest daily correlation with ground observations (median CC of 0.58 and 0.54, respectively) and the most balanced performance. Conversely, CHIRPS shows a persistent wet bias, while PERSIANN − CDR is found to be unreliable for daily applications. Crucially, the synoptic analysis identifies a recurrent atmospheric pattern strongly associated with extreme wet events: a deep upper-level trough at 200 hPa over the Eastern Mediterranean induces a low-level cyclonic anomaly that drives a robust southwesterly moisture transport from the Red Sea and Persian Gulf. By linking statistical product skill to physical atmospheric drivers, this research provides actionable intelligence for water resource management and establishes a physical basis for improving regional climate predictability.