Building on growing evidence that acting before the onset of a disaster is significantly faster, more dignified, and more (cost-)effective than response afterwards, humanitarian and governmental actors develop and implement anticipatory action frameworks. Anticipatory action requires impact-based forecasting models with appropriate thresholds to trigger pre-defined early actions that mitigate the predicted impact. Despite significant advances in hazard forecasting and the data revolution leading to more data on risk and impact as well as artificial intelligence solutions becoming available, it remains challenging to produce impact-based forecasts. This special issue aims to showcase lessons learned and best practices on impact-based multi-hazard early-warning early-action systems from the perspective of the multiple actors involved in the anticipatory action value chain. It presents novel methods to translate climate-related and geohazard forecasts into impact-based forecasts. We solicit contributions (commentaries, review articles, original research articles) from different perspectives on this interdisciplinary topic from research scientists, students, practitioners, and stakeholders. It will be a unique opportunity to further our understanding of impact-based forecasting.

Abstracts that fall under one of the following themes will be considered:

• risk or impact assessments that are undertaken to inform the co-production of impact-based forecasting models and early-action protocols and to set corresponding trigger levels, whichcan include research into how risk and impact data can be governed, ensuring, for example, the sharing of data between the multitude of data providers and data users, or how historical hazard-impact catalogues can be created;
• forecast skill analyses of the hazard forecasts from national, regional, or global forecasting sources so that there is a clear understanding of the reliability of hazard forecasts that are used in the impact-based forecasts;
• reflections on the influence of forecast uncertainty across different timescales in decision-making and on how uncertainties can best be communicated and visualized;
• impact-based forecasting modelling, ranging from composite risk index types of approaches to elementary or statistical modelling, andbenchmarking of different approaches;
• the use of state-of-the-art methods, such as using artificial intelligence, big data, and earth observation applications, to address the difficulties in the creation of hazard-impact catalogues, impact-based forecasting models, and/or beneficiary targeting;
• research work linked to people-centred early-warning systems and anticipatory action including the role of indigenous/traditional knowledge in the development and strengthening of people-centred early-warning systems, last-mile early-warning delivery, and the co-design of gender- and disability-sensitive climate services for a gender-transformative early warning;
• barriers and opportunities in the development and use of impact-based forecasting in anticipatory action systems; explanations of the role of humanitarian agencies, scientists, and communities at risk in creating standard operating procedures for economically feasible actions; examples of cost-efficient portfolios of early actions for climate-/geo-related impact preparedness such as cash transfer for droughts or weather-based insurance for floods; assessments on the types and costs of possible forecast-based disaster risk management actions; practical applications of impact forecasts.

Manuscripts with case studies in different geographic regions will be welcome. Papers that do not address these topics, if appropriate, will be proposed to the general submissions for NHESS. A series of contributions will be drawn from EGU 2023 Session HS4.5 Reducing the impacts of natural hazards through forecast-based action: from early warning to early actionand Session HS4.4 Operational forecasting and warning systems for natural hazards and climate emergency: challenges and innovations. However, unsolicited contributions are also highly encouraged. The guest editors aim for diversity and balance in contributions and authors, encouraging researchers from developing countries, women, and underrepresented minorities to contribute to this special issue.