Advances in impact modeling and numerical weather forecasting have allowed accurate drought monitoring and skilful forecasts that can drive decisions at the regional scale. State-of-the-art drought early-warning systems are currently based on statistical drought indicators, which do not account for dynamic regional vulnerabilities, and hence neglect the socio-economic impact for initiating actions. The transition from conventional physical forecasts of droughts toward impact-based forecasting (IbF) is a recent paradigm shift in early warning services, to ultimately bridge the gap between science and action. The demand to generate predictions of “what the weather will do” underpins the rising interest in drought IbF across all weather-sensitive sectors. Despite the large expected socio-economic benefits, migrating to this new paradigm presents myriad challenges. In this article, we provide a comprehensive overview of drought IbF, outlining the progress made in the field. Additionally, we present a road map highlighting current challenges and limitations in the science and practice of drought IbF and possible ways forward. We identify seven scientific and practical challenges/limitations: the contextual challenge (inadequate accounting for the spatio-sectoral dynamics of vulnerability and exposure), the human-water feedbacks challenge (neglecting how human activities influence the propagation of drought), the typology challenge (oversimplifying drought typology to meteorological), the model challenge (reliance on mainstream machine learning models), and the data challenge (mainly textual) with the linked sectoral and geographical limitations. Our vision is to facilitate the progress of drought IbF and its use in making informed and timely decisions on mitigation measures, thus minimizing the drought impacts globally.

中文翻译：

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基于影响的干旱预测的科学和实践的进展和差距

影响模型和数值天气预报的进步使得准确的干旱监测和熟练的预测成为可能，从而推动区域规模的决策。目前最先进的干旱预警系统基于统计干旱指标，没有考虑到动态的区域脆弱性，因此忽视了采取行动的社会经济影响。从传统的干旱物理预测到基于影响的预测（IbF）的转变是早期预警服务的最新范式转变，以最终弥合科学与行动之间的差距。对“天气状况”进行预测的需求支撑了所有天气敏感行业对干旱 IbF 日益增长的兴趣。尽管预期会带来巨大的社会经济效益，但迁移到这种新模式也面临着无数的挑战。在本文中，我们对干旱 IbF 进行了全面概述，概述了该领域取得的进展。此外，我们还提出了一份路线图，强调了干旱 IbF 科学和实践中当前的挑战和局限性以及可能的前进方向。我们确定了七个科学和实践挑战/限制：背景挑战（对脆弱性和暴露的空间部门动态的解释不充分）、人类-水反馈挑战（忽略人类活动如何影响干旱的传播）、类型学挑战（将干旱类型过度简化为气象）、模型挑战（依赖主流机器学习模型）以及具有相关部门和地理限制的数据挑战（主要是文本）。我们的愿景是促进干旱 IbF 的进展，并利用其在缓解措施方面做出明智、及时的决策，从而最大限度地减少全球干旱影响。