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<p>The present study evaluated the performances of IMDAA and ERA5 reanalysis in simulating monsoon precipitation extremes over Eastern India during 1979–2020. Various extreme precipitation indices from ETCCDI, such as Rx1day, Rx5day, R95pTOT, R99pTOT, CWD, SDII, and PRCPTOT, were considered to capture intensity, frequency, and duration of extreme events, which are critical for understanding climate variability and disaster risk in this region. Statistical&#xa0;indices like spatial correlation, RMSE-observations standard deviation ratio (RSR), mean absolute error (MAE), and percentage bias (PB) were utilized as performance metrics&#xa0; for the evaluation of reanalyses. Results indicated the superior performance of ERA5 reanalysis compared to IMDAA in accurately simulating extreme precipitation events. ERA5 showed higher spatial correlation and lower RSR, MAE, and PB for the majority of extreme indices. The application of Quantile Delta Mapping (QDM) substantially improved the skills of reanalysis datasets with observed precipitation patterns by reducing errors and biases and enhancing spatial correlation. These findings emphasize the importance of using bias-corrected reanalysis data to accurately assess extreme events and improve disaster management under the changing climate in Eastern India, with potential applications in similar climatic regions.</p>

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Performance analysis of IMDAA and ERA5 reanalysis in reproducing monsoon precipitation extremes over Eastern India

  • 期刊论文

  • 发表日期：

    2025年6月12日

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  • Javed Akhter,
  • Subhodip Sarkar,
  • Ratul Roy Choudhury,
  • Lalu Das,
  • Subrata Kumar Midya

摘要

The present study evaluated the performances of IMDAA and ERA5 reanalysis in simulating monsoon precipitation extremes over Eastern India during 1979–2020. Various extreme precipitation indices from ETCCDI, such as Rx1day, Rx5day, R95pTOT, R99pTOT, CWD, SDII, and PRCPTOT, were considered to capture intensity, frequency, and duration of extreme events, which are critical for understanding climate variability and disaster risk in this region. Statistical indices like spatial correlation, RMSE-observations standard deviation ratio (RSR), mean absolute error (MAE), and percentage bias (PB) were utilized as performance metrics  for the evaluation of reanalyses. Results indicated the superior performance of ERA5 reanalysis compared to IMDAA in accurately simulating extreme precipitation events. ERA5 showed higher spatial correlation and lower RSR, MAE, and PB for the majority of extreme indices. The application of Quantile Delta Mapping (QDM) substantially improved the skills of reanalysis datasets with observed precipitation patterns by reducing errors and biases and enhancing spatial correlation. These findings emphasize the importance of using bias-corrected reanalysis data to accurately assess extreme events and improve disaster management under the changing climate in Eastern India, with potential applications in similar climatic regions.