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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.

Identifying drought indices that effectively predict future drought impacts remains a critical challenge in seasonal forecasting, as these indices provide the necessary actionable information that enables stakeholders to better anticipate and respond to drought-related challenges. This study evaluates how drought indices balance forecast skill and relevance for estimating impacts across Europe. Using European Centre for Medium-Range Weather Forecasts SEAS5 seasonal predictions and ERA5 reanalysis as benchmarks, we assessed the predictability skill of drought indices over various accumulation periods and their relevance in estimating drought impacts across Europe, with the aim of enhancing impact-based forecasting. To evaluate these relationships, we built upon the findings from a study that utilized drought impact data from the European Drought Impact Report Inventory and employed random forest models to evaluate the significance of various drought indices in predicting sector-specific impacts. Our findings reveal higher predictability in Northern and Southern Europe, particularly during winter and summer, with some regions showing extended predictability up to six months, depending on the season. Focusing on case studies in the UK and Germany, our results highlight regions and seasons where accurate impact predictions are possible. In both countries, high impact predictability was found up to six months ahead, with sectors such as Agriculture, Water Supply, and Tourism in the UK, and Agriculture and Water Transportation in Germany, depending on the region and season. This analysis represents a significant step forward in identifying the most suitable drought indices for predicting impacts across Europe. Our approach not only introduces a new method for evaluating the relationship between drought indices and impacts, but also addresses the challenge of selecting indices for estimating impacts. This framework advances the development of operational impact-based drought forecasting systems for Europe.

Despite the scientific progress in drought detection and forecasting, it remains challenging to accurately predict the corresponding impact of a drought event. This is due to the complex relationships between (multiple) drought indicators and adverse impacts across different places/hydroclimatic conditions, sectors, and spatiotemporal scales. In this study, we explored these relationships by analyzing the impacts of the severe 2018-2019 central European drought event in Germany. We first computed the standardized precipitation index (SPI), the standardized precipitation evaporation index (SPEI), the standardized soil moisture index (SSMI) and the standardized streamflow index (SSFI) over various accumulation periods, and then related these indicators to sectorial losses from the European drought impact report inventory (EDII) and media sources. To cope with the uncertainty associated with both drought indicators and impact data, we developed a fuzzy method to categorize them. Lastly, we applied the method at the region level (EU NUTS1) by correlating monthly time series. Our findings revealed strong and significant relationships between drought indicators and impacts over different accumulation periods, albeit in some cases region-specific and time-variant. Furthermore, our analysis established the interconnectedness between various sectors, which displayed systematically co-occurring impacts. As such, our work provides a new framework to explore drought indicators-impacts dependencies across space, time, sectors, and scales. In addition, it emphasizes the need to leverage available impact data to better forecast drought impacts.

Accurate drought impact-based forecasting of crop yield in India remains challenging due to the country’s hydro-climatic diversity and complex interactions between climate variability, ecosystem vulnerability, and agriculture. This study develops a framework integrating observed and forecasted drought indices across multiple accumulation periods to predict standardised crop yields at seasonal lead time before planting. Using district-level and cluster-based approaches, we apply Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks to establish indicator–impact relationships for paddy rice (wet season) and wheat (dry season), leveraging historical yield data and seasonal forecasts.

District-level models outperform cluster-based ones, with Random Forest showing the best performance. Over 80% of wheat districts and 70% of rice districts achieve strong predictive accuracy, defined as RMSE below 0.2 in the test set. Incorporating ECMWF’s SEAS5 forecasts enables reliable rice yield predictions up to six months before the season—covering over 80% of wheat districts and 60–70% of rice districts. Forecast skill assessed using Continuous Ranked Probability Score (CRPS) confirms robustness across space and time, especially in districts with moderate yield variability. Weighted CRPS shows forecasts for extremely low yields (below the 10th percentile) are accurate and reliable—crucial for early warning and preparedness.

This work advances operational impact-based drought forecasting in India, offering a tool to inform anticipatory action among farmers, water managers, and supply chains. By linking drought observations and seasonal forecasts to crop yield outcomes, the study provides a replicable early warning approach to support targeted mitigation and enhance climate resilience in agriculture.