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<p>To mitigate estimation biases of CMIP6 climate models in simulating extreme precipitation, this study proposes an advanced bias correction framework that synergistically integrates multi-model advantages. The methodology involves three key innovations: (1) developing a polynomial-based empirical relationship to establish non-linear response functions; (2) coupling support vector machine algorithms for time series simulation; and (3) implementing parametric quantile mapping for the error calibration of Pearson Type III distribution. Applied to extreme precipitation indices (RX1day, RX5day, R95p, R99p) in the Hanjiang River Basin (HRB), the framework demonstrates substantial performance improvements: correlation coefficients (CC) increase from −&#xa0;0.11–0.60 to 0.72–0.79, Nash–Sutcliffe efficiency (NSE) improves from −&#xa0;8.17–−&#xa0;0.10 to 0.50–0.62, and relative bias (RB) decreases from −&#xa0;1.33–3.22 to 0.93–2.47%. Subsequent parametric quantile mapping further reduces RB to 0.22–0.80% without compromising CC/NSE metrics, effectively addressing total volume estimation errors. Through analysis of error-corrected extreme precipitation data, it reveals that under moderate to high radiative forcing scenarios, the HRB will experience significant upward trends in extreme precipitation, particularly marked by changes in the RX1day and R95p indices. High-incidence zones of extreme precipitation are identified in the southwestern sector of the basin and the downstream areas of HRB, which exhibit heightened sensitivity to climate change. As radiative forcing intensifies, the upward trends in extreme precipitation within these regions become increasingly pronounced, suggesting a growing risk of flood-inducing. This research addresses limitations in current climate model outputs, enhances the predictive capability of climate models for extreme precipitation events, and provides a scientific foundation for hydrological forecasting and flood risk mitigation in the basin.</p>

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Multi-model integrated error correction for extreme precipitation: method and application

  • 期刊论文

  • 发表日期：

    2026年3月26日

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  • Deng Pengxin,
  • Bing Jianping,
  • Zhang Dongdong,
  • Li Wei

摘要

To mitigate estimation biases of CMIP6 climate models in simulating extreme precipitation, this study proposes an advanced bias correction framework that synergistically integrates multi-model advantages. The methodology involves three key innovations: (1) developing a polynomial-based empirical relationship to establish non-linear response functions; (2) coupling support vector machine algorithms for time series simulation; and (3) implementing parametric quantile mapping for the error calibration of Pearson Type III distribution. Applied to extreme precipitation indices (RX1day, RX5day, R95p, R99p) in the Hanjiang River Basin (HRB), the framework demonstrates substantial performance improvements: correlation coefficients (CC) increase from − 0.11–0.60 to 0.72–0.79, Nash–Sutcliffe efficiency (NSE) improves from − 8.17–− 0.10 to 0.50–0.62, and relative bias (RB) decreases from − 1.33–3.22 to 0.93–2.47%. Subsequent parametric quantile mapping further reduces RB to 0.22–0.80% without compromising CC/NSE metrics, effectively addressing total volume estimation errors. Through analysis of error-corrected extreme precipitation data, it reveals that under moderate to high radiative forcing scenarios, the HRB will experience significant upward trends in extreme precipitation, particularly marked by changes in the RX1day and R95p indices. High-incidence zones of extreme precipitation are identified in the southwestern sector of the basin and the downstream areas of HRB, which exhibit heightened sensitivity to climate change. As radiative forcing intensifies, the upward trends in extreme precipitation within these regions become increasingly pronounced, suggesting a growing risk of flood-inducing. This research addresses limitations in current climate model outputs, enhances the predictive capability of climate models for extreme precipitation events, and provides a scientific foundation for hydrological forecasting and flood risk mitigation in the basin.