Climate adaptation must be intensified globally to keep up with increasing climate change impacts. To date, adaptation is mostly driven by climate risks that need to be reduced and little by opportunities. We identify opportunity windows -i.e. favourable conditions for new or upgraded measures- by constructing an evidence-based dataset from qualitative and quantitative sources, and discuss how they can help to progress adaptation. Here we find evidence of past, present and future adaptation opportunity windows throughout Europe. Present and past triggers for opportunity windows include the implementation of European Union Directives and post-disaster recovery funds, and societal support after floods, droughts and wildfires. Future opportunities relate to aging dams and storm surge barriers. Opportunities also emerge from innovations (e.g. through pilots) which could inform new forms of adaptation. Decision-makers can operationalise and seize adaptation opportunities through adaptive pathways planning, but would need to anticipate opening and closing windows.

Climate attribution is a rapidly evolving field quantifying the effect of climate change on extreme weather events. Findings from attribution studies often fuel news headlines to inform on the consequences of climate change. Moreover, the field of climate attribution has become an important knowledge base to build our understanding of how the likelihood and intensity of extreme events is affected by climate change. However, the applicability of climate attribution for allocating Loss & Damage funds, climate litigation cases, and informing adaptation decisions remains a matter of debate. For example for adaptation planning, Osaka & Bellamy (2020) showcase diverging views on the usefulness of climate attribution between scientists, stakeholders, and citizens. Other studies argue that by purely focusing on meteorological drivers, climate attribution ignores other, perhaps more impactful, drivers of disasters, such as unplanned urban expansion into flood-prone areas.A newer field, rising from climate attribution, is the field of impact attribution that aims to quantify the effect of climate change on impact, thereby including exposure and vulnerability. It can be argued that propagating extreme weather events to impact aligns closer with the lived experience of society, thereby providing a more holistic analysis. By including other components of risk, one could argue that impact attribution becomes useful for adaptation, while climate attribution might be more useful for incentivizing climate mitigation.During this session, we invite you to reflect on the purpose, usefulness, and future directions of impact attribution. Key questions include: Does impact attribution complement or replace climate attribution? Should it incorporate non-climate drivers, and is it feasible for all hazard types, including compound events? What is needed for the field to reach its full potential? In a rapidly developing field where much remains open, structured reflection on these foundations is not a distraction from progress, but essential for guiding it.

Urban climate change adaptation action is urgently needed in light of escalating climate change impacts. As the primary scientific assessment body for climate change, the Intergovernmental Panel on Climate Change (IPCC) provides crucial knowledge to support global, national and sub-national governance efforts. This paper investigates the pathways via which IPCC affects climate policy, focusing on its role in urban adaptation action. Through a literature review and two case studies in India and the Netherlands, we captured important elements in IPCC policy impact pathways along the dimensions of inputs, outputs and outcomes. We used scientometric analysis of urban policy documents as well as interviews with local policy actors to study whether IPCC knowledge is considered credible, relevant and legitimate (CRELE) to explain its uptake for local adaptation policy processes and outputs. We find that actors in both cities consider IPCC as a highly credible and legitimate knowledge authority, leading to its important role in shaping policy. The IPCC’s relevance and consequently the extent of its influence diverged with local knowledge needs and governance systems, with the Netherlands case study showing substantial, yet indirect IPCC influences, and the India case study more frequent but less extensive impacts. Our results show a variety of impact pathways from the IPCC’s credible knowledge and widely accepted scientific authority: identifying problems, framing issues, inspiring action and legitimizing policy decisions. To further enhance the IPCC’s applicability to diverse contexts while respecting its policy-neutral mandate, several recommendations are made, particularly for increased relevance to the Majority World.

While climate change continues to exacerbate extreme weather events worldwide, their impacts can be further amplified or dampened by socio-economic drivers that influence local exposure and vulnerability. These drivers, such as population growth and economic development, are dynamic and influence the impact of extreme events on global and local scales. For example, population growth in flood-prone areas can heighten exposure, whereas economic development can increase economic losses while improving the capacity to cope with a disaster and thus reduce vulnerability. A novel method to quantify the effect of climate change and socio-economic drivers on the impact of these events is through impact attribution assessments.This research expands a storyline attribution framework for quantifying the effects on climate change on the hazard and impact of compound flooding to also include the effect of socio-economic drivers. An event-based approach for compound flooding from tropical cyclone (TC) Idai in Mozambique is used to disentangle the effect of historical climate and population change. TC Idai hit Mozambique in 2019 and caused over 600 fatalities, affected over 1.8 million people, resulting in $3 billion in damages. Idai is used as a case study, representing an extremely destructive compound flood event in a underrepresented, data-poor, and highly vulnerable region.Compound flooding is modelled using a state-of-the-art hydrodynamic modelling chain that combines the Super-Fast INundation for coastS (SFINCS) model with the hydrodynamic model Delft3D Flexible Mesh and hydrological model wflow. The climate drivers of compound flooding from TCs that are known to be affected by climate change, such as precipitation, wind and sea-level rise, are adjusted to create scenarios with the climate trend removed. Present-day and historical population scenarios are used as exposure data to assess the effect of population change based on a novel harmonized global population dataset (Paprotny, 2025). By modelling multiple factual and counterfactual scenarios, in which drivers are adjusted individually and jointly, we disentangle their respective contributions to the affected population and fatalities.This approach advances event-based impact attribution by incorporating non-climatic drivers into assessments of compound flood impacts from TCs. The framework relies solely on global datasets and open-source software which allows worldwide applications, including highly impacted but data-scarce and often underrepresented regions.

In 2012, soybean crops failed in the three largest producing regions due to spatially compounded hot and dry weather across North and South America. Here, we present different impact storylines of the 2012 event, calculated by combining a statistical crop model with climate model simulations of 2012 conditions under pre-industrial, present-day (+1 degrees C), and future (+2 degrees C) conditions. These simulations use the ECHAM6 climate model and maintain the same observed seasonally evolving atmospheric circulation. Our results demonstrate that anthropogenic warming strongly amplifies the impacts of such a large-scale circulation pattern on global soybean production. Although the drought intensity is similar under different warming levels, larger crop losses are driven not only by warmer temperatures but also by stronger heat-moisture interactions. We estimate that one-third of the global soybean production deficit in 2012 is attributable to anthropogenic climate change. Future warming (+2 degrees C above pre-industrial) would further exacerbate production deficits by one-half compared to present-day 2012 conditions. This highlights the increasing intensity of global soybean production shocks with warming, requiring urgent adaptation strategies.

It is widely recognized that climate change is altering the likelihood and intensity of extreme weather events globally, including hydrological extremes such as floods. Compound flooding is driven by fluvial, pluvial and coastal flooding occurring simultaneously, resulting in a potentially larger impact when co-occurring than the sum of the univariate drivers happening separately. Identifying and communicating the effect of climate change on compound flooding remains challenging. A method to quantify the effect of climate change on these events is through climate attribution assessments. This research assesses how existing climate attribution methods can be applied to compound events instead of univariate events. An event-based storyline attribution approach for compound flooding from historical tropical cyclones (TCs) in Mozambique is used to examine the effect of climate change on multiple flood drivers propagated to impact. TC Idai hit Mozambique in 2019 and caused over 600 fatalities, affected over 1.8 million people, resulting in $3 billion in damages. Idai is used as a case study, representing a highly destructive compound flood event. Compound flooding is modelled using a state-of-the-art hydrodynamic modelling chain that combines the Super-Fast INundation for coastS (SFINCS) model with the hydrodynamic model Delft3D Flexible Mesh and hydrological model wflow, linked to a fast impact assessment tool Delft-FIAT to calculate the flood impact, here the direct economic damages. The drivers of compound flooding from TCs that are known to be affected by climate change, such as precipitation, wind and sea-level rise, are adjusted to create counterfactual scenarios. The compound flooding is modelled for the multiple factual and counterfactual scenarios, adjusting the separate drivers individually and simultaneously. This approach enables the attribution of climate change effects on compound flooding from TCs while identifying potential changes in the contributions of individual flood drivers. Next steps include attribution uncertainty partitioning, comparing multiple climate attribution approaches for these events, assessing regional differences with relation to climate change effects on compound flood impact and comparing this methodology for multiple TCs in the same region, which may have different driver contributions.

We see unprecedented weather causing widespread impacts across the world. In this perspective, we provide an overview of methods that help anticipate unprecedented weather hazards that can contribute to stop being surprised. We then discuss disaster management and climate adaptation practices, their gaps, and how the methods to anticipate unprecedented weather may help build resilience. We stimulate thinking about transformative adaptation as a foundation for long-term resilience to unprecedented weather, supported by incremental adaptation through upgrading existing infrastructure, and reactive adaptation through short-term early action and disaster response. Because in the end, we should take responsibility to build resilience rather than being surprised by unprecedented weather.

Recent disasters have highlighted the severe local impacts of extreme precipitation, including flash floods, landslides, and urban inundation. Despite significant investments in early warning systems, these events often catch many people off guard, emphasizing the need for a better translation of warnings into early actions. In this study, we directly address this gap by translating precipitation forecasts from the ECMWF, specifically the extreme forecast index (EFI) and shift of tails (SOT), to concrete triggers for early action. Our analysis reveals that such actions, triggered with a 2-3-day lead time, have a high potential economic value (PEV) across large parts of Europe. The SOT forecasts generally have higher economic value than EFI, especially at longer lead times. However, the effectiveness of both disappears across most of Europe with a 5-day lead. These results are based on comparing forecasts to the E-OBS dataset for extreme rainfall (>5-yr return period) over 8years. We apply the optimal warning thresholds found in this analysis to the rainfall event that triggered the July 2021 western Europe flood disaster. Our results indicate that the EFI and SOT forecasts provided accurate and timely warnings at least 2-3 days in advance, aligning with flood impacts recorded in the Emergency Events Database (EM-DAT) dataset. Notably, on a 1-day lead time, the SOT forecasts accurately pointed to the Ahr catchment in Germany with highly exceptional values, providing strong indications of the disaster that unfolded there. This study underscores the value of these rainfall indicators and calls for further testing on more high-impact events. SIGNIFICANCE STATEMENT: Recent floods triggered by extreme rainfall, such as the 2021 western Europe floods, the 2023 Emilia-Romagna floods, and the 2022-23 California floods, have had an enormous impact. These events highlight the urgent need to scale up early actions. In this study, we use two rainfall indicators from ECMWF: the extreme forecast index (EFI) and the shift of tails (SOT). We find evidence that these indicators have high economic value when used to trigger early actions 2-3 days ahead of extreme rainfall events in Europe. Furthermore, these indicators provided accurate warnings at least 2 days before the 2021 western Europe floods. This underscores the vital role of early warning and preparedness in reducing future disaster risk.

The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and discuss core research problems in information retrieval to assess climate change impacts. The workshop aimed to foster collaboration by bringing communities together that have so far not been very well connected – information retrieval, natural language processing, systematic reviews, impact assessments, and climate science. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for information retrieval to assess climate change impacts.

Vegetation is a relevant and highly dynamic component of the Earth system and its variability – at seasonal, interannual, decadal and longer timescales – modulates the coupling with the atmosphere by affecting surface variables such as roughness, albedo and evapotranspiration. In this study, we investigate the effects of improved representation of vegetation dynamics on climate predictability and prediction at the seasonal timescale. To this aim, the observational constraints from the latest generation satellite dataset of vegetation Leaf Area Index (LAI) have been integrated in the modeling, including a parameterization of the effective vegetation cover as a function of LAI. The improved vegetation representation is implemented in HTESSEL, which is the land surface model included in the seasonal forecasting (ECMWF SEAS5) systems used in this work. Our results show that the realistic representation of vegetation variability has significant effects on both potential predictability and actual prediction skill at the seasonal time scale. It is shown a significant improvement of the skill in predicting boreal winter (December-January-February; DJF) 2m Temperature (T2M) at 1-month lead time especially over Euro-Asian boreal forests; the improvement is at least in part due to the more realistic representation of the interannual albedo variability that is related to the changes in vegetation shading over snow. Remarkably, from the region with the most considerable T2M improvement originates a large-scale ameliorating effect on circulation encompassing Northern Hemisphere middle-to-high latitudes from Siberia to the North Atlantic. The results indicate that the coupling with the improved vegetation might operate by amplifying locally the signal originating from the North Atlantic sector, therefore improving both potential predictability and actual skill over the region. Concurrently, the improved predictability and skill over the Euro-Asian forests appears to feedback to the large-scale circulation enhancing the representation of the circulation pattern and associated interannual anomalies.

As global climate patterns shift, Europe faces increasing challenges from key risks such as floods. However, translating this knowledge into locally usable risk information presents a significant challenge. A primary reason is the large variability associated with climate projection outcomes, particularly in precipitation patterns. This paper introduces a seasonal decision-scaling approach to identify decision-relevant climate storylines for regional discharge patterns, which are crucial in assessing flood risks. We sample scenarios within the uncertainty space of future projections and employ a statistical weather generator to determine probabilistic flow changes. Through the analysis of flow changes across various climate scenarios, we identify the most impactful seasonal climate parameters. These parameters are then used to cluster Global Climate Models, from which we create a set of decision-relevant climatological storylines for floods. A case study in Latvia demonstrates that river flows depend on only a few key seasonal parameters, indicating that the uncertainty can be effectively captured with a select number of distinct climatological storylines. This study not only simplifies the complexity of analysing future climate risks but also enhances the practicality of climate information at the regional level. Our novel seasonal approach to decision-scaling and the selection of decision-relevant climate storylines can be applied globally in areas where GCMs indicate varying climate trends and can also be used for drought analyses. This methodology leads to simpler climate risk information, thereby fostering improved and more robust adaptation strategies.

Extreme weather events of unprecedented intensity in historical records can have major impacts on society and ecosystems. While adaptation plans often consider past trends in extreme weather events, few consider the possibility of exceptional extremes. This oversight leaves society underprepared and ill-equipped to handle ‘surprising’ events. There is a long history of science inquiry into the question of what low likelihood weather events are possible. Here, we present an overview of the methods used to identify exceptional weather events. We discuss tools for scientists, practitioners and policy-makers to ‘see the unseen’ and evaluate unexpected yet plausible disruptive events. We first discuss existing approaches for estimating rare extremes; then give an example for exceptional heat in The Netherlands; and finally outline how this knowledge can be leveraged to strengthen resilience and adaptation efforts.

The United States (US), Brazil, and Argentina collectively produce about 75% of the world's soybean supply. In 2012, soybean crops failed in these three major producing regions due to spatially compound hot and dry weather across North and South America. This led to unprecedented shortages in the global supply, resulting in record-high market prices. Despite the severity of this event, the role of historical and future anthropogenic warming in influencing such occurrences remains unknown. Here, we present different impact storylines of the 2012 event by imposing the same seasonally evolving atmospheric circulation in a pre-industrial, present day (+1°C above pre-industrial), and future (+2°C above pre-industrial) climate. We use so-called nudged atmospheric simulations and train a statistical model to estimate yield losses from meteorological conditions. While the drought intensity is rather similar under different warming levels, our results show that anthropogenic warming strongly amplifies the impacts of such a large-scale circulation pattern on global soybean production, driven not only by warmer temperatures, but also by stronger heat-moisture interactions. We estimate that 51% (47-55%) of the global soybean production deficit in 2012 is attributable to climate change. Future warming (+2°C above pre-industrial) would further exacerbate production deficits by 58% (46-67%), compared to present-day 2012 conditions. This highlights the increasing intensity of global soybean production shocks linked to similar atmospheric conditions with warming and thus requires urgent adaptation strategies.

While high impact weather events pose considerable challenges to society, we have limited understanding of their risks and potential impacts due to their rare nature. Climate change, in combination with internal climate variability, increases the uncertainty around these events and their impacts in the future. Storylines offer a non-probabilistic approach into estimating and understanding such events and their impacts conditioned on specific assumptions and scenarios, such as climate change and internal climate variability. Our study presents storylines of Hurricane Sandy (2012) to assess compound coastal flooding's impact on New York City's critical infrastructure under different scenarios. These include the effects of climate change, such as changes in storm dynamics and sea-level rise, as well as internal climate variability, accounting for variations in storm intensity and location. We use a comprehensive modelling framework, spanning from the driving climatological conditions to compound flooding and societal impacts. Our findings indicate that a 1m sea level rise could increase flood volumes by an average of 4.2 times, while internal climate variability could lead to a 2.5-fold increase in flood volumes. We find that impacts on critical infrastructure depend not only on flood volume, but also on the predominant flood hazard in each storyline, like storm surge or local precipitation. This study highlights the importance of developing societal-relevant scenarios that consider both climate change and internal variability. Such scenarios, coupled with a comprehensive modelling framework, provide useful information for decision making when preparing for high impact events in the future.

Food insecurity is a global concern resulting from various complex processes and a diverse range of drivers. Due to its complexity, it is one of the most challenging drought impacts to predict. In this study, we introduce a novel machine learning model designed to forecast food crises in the Horn of Africa up to 12 months in advance. We trained an “XGBoost” model using more than 20 different input datasets to capture key food security drivers such as drought, economic shocks, conflicts and livelihood vulnerability. The model shows a promising ability to predict food security dynamics several months in advance (R2>0.6, three months in advance). Notably, it accurately predicted 20% of crisis onsets in pastoral regions (n = 84) and 40% of crisis onsets in agro-pastoral regions (n = 23) with a 3-month lead time. We compared these results to the established FEWS NET early warning system, and found a similar performance over these regions. However, our model is clearly less skilled in predicting food security for crop-farming regions than FEWS NET. This study underscores the importance of integrating machine learning into operational early-warning systems like FEWS NET and expanding these techniques to the continental or global-scale.

Climate change and sustainability linkages provide opportunities to develop and implement synergistic strategies for climate actions and achieve Sustainable Development Goals (SDGs). Thus, we synthesize the coverage of SDGs in the IPCC’s fifth and sixth assessment reports at a target level. Based on this reflection, we propose the seventh assessment cycle of the IPCC to cover SDGs holistically, using asystematic approach, breaking the Working Group silos, and contributing to the post-2030 agenda.

Coastal settlements, facing increasing flood risk from tropical cyclones (TCs) under climate change, need local and detailed climate information for effective adaptation. Analysis of historical events and their impacts provides such information. This study uses storylines to evaluate adaptation strategies, focusing on cyclone Idai's impact on Beira, Mozambique, under different climate conditions and tidal cycles. A storyline of Idai under 3 degrees C warming increases flood impacts by 1.8 times, while aligning Idai with spring tides amplifies these by 21 times. Combining both conditions increases impacts beyond 37 times. An adaptation strategy combining flood protection and accommodation measures reduces impacts by maximum 83%, while a seawall strategy reduces these by 10%. By offering localized, detailed information, storylines can be used to measure the effectiveness of adaptation strategies against extreme events, evaluating their robustness across different scenarios, and quantifying residual impacts, complementing traditional climate risk assessments for informed decision-making.

The MANILA25 workshop is aimed at collaborative agenda setting around the general area of information retrieval for climate impact in general and around adaptation tracking in particular. To this end, the workshop starts by creating a shared understanding of the problem space through invited talks around information needs in climate impact, search and analysis of climate impact literature, adaptation tracking, and resources. It then brings in different perspectives on the four topics through a number of brief ''flash'' presentations by participants in the workshop. Then, the participants will work to co-develop a research agenda in a small-scale, highly interactive setting.