** Progress in Earth and Planetary Science is the official journal of the Japan Geoscience Union, published in collaboration with its 50 society members.

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    • Progress in Earth and Planetary Science
    • Progress in Earth and Planetary Science
    • Progress in Earth and Planetary Science
    • Progress in Earth and Planetary Science
    • Progress in Earth and Planetary Science
    Progress in Earth and Planetary Science

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    Atmospheric and hydrospheric sciences

    Coupling dynamical and statistical downscaling for high-resolution rainfall forecasting: case study of the Red River Delta, Vietnam

    Anh Q T, Taniguchi K

    Dynamical downscaling, Statistical downscaling, ANN, WRF, Rainfall

    Spatial distribution of cumulative rainfall (mm) in JJA of 2006

    The hybrid dynamical-statistical downscaling approach is an effort to combine the ability of dynamical downscaling to resolve fine-scale climate changes with the low computational cost of statistical downscaling. In this study, we propose a dynamical-statistical downscaling technique by incorporating a regional climate model (RCM) with artificial neural networks (ANN) to downscale rainfall information over the Red River Delta in Vietnam. First, dynamical downscaling was performed with an RCM driven by the reanalysis to produce nested 30- and 6-km resolution simulations. Subsequently, the 6-km simulation was compared to rain gauge data to examine the ability of the RCM to reproduce known climate conditions. Then, in the statistical downscaling step, the ANN was trained to predict rainfall in the 6-km domain based on weather predictors in the 30-km simulation. Statistical downscaling results were compared with the original output from RCM to determine the accuracy of the coupling method. A bias correction method to locate no-rainfall events in the ANN downscaling result was also developed to enhance the credibility of the final results. The outcomes of this study illustrate that ANN can produce RCM-like results (r > 0.9) at a fraction of the cost, with an 89% reduction in the required computational power.