While reliable drought prediction is fundamental for drought mitigation and water resources management, it is still a challenge to develop robust drought prediction models due to complex local hydro-climatic conditions and various predictors. Sea surface temperature (SST) is considered as the fundamental predictor to develop drought prediction models. However, traditional models usually extract SST signals from one or several specific sea zones within a given time span, which limits full use of SST signals for drought prediction. Here, we introduce a new meteorological drought prediction approach by using the antecedent SST fluctuation pattern (ASFP) and machine learning techniques (e.g., support vector regression (SVR), random forest (RF), and extreme learning machine (ELM)). Three models (i.e., ASFP-SVR, ASFP-ELM, and ASFP-RF) are developed for ensemble, probability, and deterministic drought predictions. The Colorado, Danube, Orange, and Pearl River basins with frequent droughts over different continents are selected, as the cases, where standardized precipitation evapotranspiration index (SPEI) are predicted at the 1° × 1° resolution with 1- and 3-month lead times. Results show that the ASFP-ELM model can effectively predict space-time evolutions of drought events with satisfactory skills, outperforming the ASFP-SVR and ASFP-RF models. Our study has potential to provide a reliable tool for drought prediction, which further supports the development of drought early warning systems.

中文翻译：

---

使用先行海温波动和机器学习进行稳健的气象干旱预测

虽然可靠的干旱预测是缓解干旱和水资源管理的基础，但由于当地复杂的水文气候条件和各种预测因素，开发强大的干旱预测模型仍然是一项挑战。海面温度 (SST) 被认为是开发干旱预测模型的基本预测指标。然而，传统模型通常在给定的时间跨度内从一个或几个特定海区提取海温信号，这限制了海温信号在干旱预测中的充分利用。在这里，我们通过使用先行海温波动模式（ASFP）和机器学习技术（例如，支持向量回归（SVR）、随机森林（RF）和极限学习机（ELM））介绍了一种新的气象干旱预测方法。三种模型（即 ASFP-SVR、ASFP-ELM、和 ASFP-RF) 是为集合、概率和确定性干旱预测而开发的。选择不同大陆干旱频繁的科罗拉多、多瑙河、奥兰治和珠江流域作为案例，其中标准化降水蒸散指数 (SPEI) 在 1° × 1° 分辨率下预测，提前 1 个月和 3 个月次。结果表明，ASFP-ELM模型能够有效预测干旱事件的时空演变，并具有令人满意的技能，优于ASFP-SVR和ASFP-RF模型。我们的研究有可能为干旱预测提供可靠的工具，从而进一步支持干旱预警系统的发展。在这种情况下，标准化降水蒸散指数 (SPEI) 以 1° × 1° 的分辨率进行预测，提前期为 1 个月和 3 个月。结果表明，ASFP-ELM模型能够有效预测干旱事件的时空演变，并具有令人满意的技能，优于ASFP-SVR和ASFP-RF模型。我们的研究有可能为干旱预测提供可靠的工具，从而进一步支持干旱预警系统的发展。在这种情况下，标准化降水蒸散指数 (SPEI) 以 1° × 1° 的分辨率进行预测，提前期为 1 个月和 3 个月。结果表明，ASFP-ELM模型能够有效预测干旱事件的时空演变，并具有令人满意的技能，优于ASFP-SVR和ASFP-RF模型。我们的研究有可能为干旱预测提供可靠的工具，从而进一步支持干旱预警系统的发展。