We consider stochastic weather models originally developed for rainfall simulations to build a hierarchical Bayesian mixture model for daily rainfall forecasts using endogenous and external information. We model daily rainfall as a seasonal-varying mixture of a Bernoulli distribution for rainfall occurrence and a gamma distribution for the rainfall amount. The model scheme allows the inclusion of predictors to reduce the bias and variance of the forecasts, while the hierarchical Bayesian framework promotes a better understanding and reduction in parameter uncertainties, especially for gauges with short records, as well as supports the estimation of regional parameters that could be employed for forecasts at ungauged sites. The model was tested using 47 years (1973–2019) of daily rainfall data from 60 gauges in South Korea. Climate indices derived from the low-level wind over the region were analyzed using Principal Component Analysis (PCA) and embodied into the model to enhance its forecast skills. The model structure was based on a detailed exploratory data analysis, which included the application of Self-Organizing Maps (SOM) to examine the spatio-temporal patterns of rainfall. Cross-validated results reveal improved skills over reference models based on climatology and persistence up to a three days lead time. The average gains in metrics such as the Brier and Winkler skill scores vary from 5% to 50%, while the average correlation skill between predictions and observations reach values up to 0.55. The gains beyond a three days lead time are marginal, but the underlying structure of the proposed model still encourages its use over the reference models, being a step forward in improving real-time daily rainfall forecasts for the region. It has also a great potential to be combined with weather model forecasts and applied in other places across the world.

我们考虑最初为降雨模拟而开发的随机天气模型，以使用内部和外部信息为每日的降雨构建分层的贝叶斯混合模型。我们将每日降雨建模为伯努利分布（用于出现降雨）和伽马分布（用于降雨量）的季节性变化混合物。该模型方案允许包含预测变量，以减少预测的偏差和方差，而分层贝叶斯框架则可以促进对参数不确定性的更好理解和减少，尤其是对于短记录的量具，并支持对区域参数的估计，可以在未开通的站点进行预测。该模型使用来自韩国60个仪表的47年（1973-2019）的每日降雨量数据进行了测试。使用主成分分析（PCA）对源自该地区低空风的气候指数进行了分析，并将其体现在该模型中以增强其预报技能。该模型结构基于详细的探索性数据分析，其中包括应用自组织图（SOM）来检查降雨的时空格局。交叉验证的结果表明，与基于气候和持久性的参考模型相比，该模型具有高达3天的交付时间，从而提高了他们的技能。诸如Brier和Winkler技能得分之类的指标的平均收益从5％到50％不等，而预测和观察值之间的平均相关技能达到的值高达0.55。交货时间超过三天的收益微不足道，但建议的模型的基本结构仍鼓励将其用于参考模型，是改善该地区实时每日降雨预报的一步。它与气象模型预测相结合并在世界其他地方应用也具有很大的潜力。