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<p>This study evaluates the performance of dynamically downscaled seasonal precipitation forecasts over Vietnam using the non-hydrostatic Regional Climate Model version 4.9.5 (RegCM-NH) driven by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The analysis examines both the raw downscaled output (RegCM_CFS) and the improvements obtained through statistical bias correction. The results reveal pronounced regional contrasts in forecast skill across seven climatic sub-regions of Vietnam, with regional climate characteristics exerting a stronger influence on performance than forecast lead time (1–6 months). Northern sub-regions (R1–R4) are better represented in terms of mean seasonal precipitation structure, while southern sub-regions (R5–R7) exhibit higher skill in reproducing interannual variability during their climatologically dominant rainy seasons. Two bias-correction methods—a climatological adjustment (CLIM) and multiple linear regression (MLR)—are applied and shown to substantially improve forecast accuracy. Among them, MLR provides the most robust and consistent enhancement by reducing systematic biases, increasing agreement with observed interannual variability, and improving spatial coherence across sub-regions. The persistence of these improvements across lead times highlights the benefit of combining dynamical downscaling with statistical post-processing for long-lead seasonal precipitation forecasting. Overall, this integrated framework offers a practical and computationally efficient approach for improving seasonal precipitation prediction over Vietnam, with direct relevance for agriculture, water resource management, and disaster preparedness.</p>

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Seasonal precipitation prediction over Vietnam: evaluation of RegCM dynamical downscaling and statistical bias correction of NCEP CFS forecasts

  • 期刊论文

  • 发表日期：

    2026年3月13日

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  • Ha Pham-Thanh,
  • Tan Phan-Van,
  • Thanh Nguyen-Xuan,
  • Dzung Nguyen-Le

摘要

This study evaluates the performance of dynamically downscaled seasonal precipitation forecasts over Vietnam using the non-hydrostatic Regional Climate Model version 4.9.5 (RegCM-NH) driven by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The analysis examines both the raw downscaled output (RegCM_CFS) and the improvements obtained through statistical bias correction. The results reveal pronounced regional contrasts in forecast skill across seven climatic sub-regions of Vietnam, with regional climate characteristics exerting a stronger influence on performance than forecast lead time (1–6 months). Northern sub-regions (R1–R4) are better represented in terms of mean seasonal precipitation structure, while southern sub-regions (R5–R7) exhibit higher skill in reproducing interannual variability during their climatologically dominant rainy seasons. Two bias-correction methods—a climatological adjustment (CLIM) and multiple linear regression (MLR)—are applied and shown to substantially improve forecast accuracy. Among them, MLR provides the most robust and consistent enhancement by reducing systematic biases, increasing agreement with observed interannual variability, and improving spatial coherence across sub-regions. The persistence of these improvements across lead times highlights the benefit of combining dynamical downscaling with statistical post-processing for long-lead seasonal precipitation forecasting. Overall, this integrated framework offers a practical and computationally efficient approach for improving seasonal precipitation prediction over Vietnam, with direct relevance for agriculture, water resource management, and disaster preparedness.