Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large‐scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high‐quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

中文翻译：

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季节性干旱预测：进展，挑战和未来前景

干旱预测对于干旱管理的预警至关重要。这篇综述提供了基于统计，动态和混合方法的干旱预测综合报告。统计干旱预测是通过对相关干旱指数与一系列潜在预测因子之间的关系进行建模来实现的，其中潜在预测因子包括大规模气候指数，当地气候变量和土地初始条件。动态气象干旱预测依赖于一般循环模型（GCM）的季节性气候预测，该模型可用于驱动农业和水文干旱预测的水文模型，其可预测性由气候强迫和初始条件共同决定。在自然周期和人为因素导致的干旱提前期长和环境变化的情况下，仍然存在挑战。改善干旱预测的未来研究前景包括但不限于高质量的数据同化，具有与干旱发生有关的关键过程的改进模型开发，用于选择或加权集合的最佳总体预报以及将统计和动态合并的混合干旱预测预测。