There is a crucial societal need to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth’s climate system, including capabilities for early warning of potential high-impact events and changes. To meet this need, the climate science community needs to provide actionable scientific information that meets the evolving needs of societies all over the world. This goal is shared by the World Climate Research Programme (WCRP) Lighthouse Activity on Explaining and Predicting Earth System Change (EPESC) and by the WCRP’s core project Atmospheric Processes and their Role in Climate (APARC) Large Ensembles for Attribution of Dynamically-driven ExtRemes (LEADER) activity.

In pursuit of this goal, this special issue welcomes research that addresses prediction, projection, and attribution of phenomena and events acting on annual to decadal timescales. This includes evaluating existing and developing new methodologies to attribute and predict changes and extremes driven by the atmospheric or oceanic circulation. We also seek advances that can lead to more reliable quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization and to improved projections.

While early warnings of changes in the Earth system are now potentially possible through, e.g., operational decadal predictions, there are several challenges: there is a lack of understanding of the dynamical mechanisms that enable such projections, and there is evidence that global models underestimate some predictable signals and that there are systematic discrepancies between simulated and observed trends. The special issue seeks research that highlights these challenges and improves our understanding of the causes of regional climate changes, needed both to attribute recent events and to gain further confidence in forecasts.

A particular focus of this special issue is papers that analyze output from the Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP; Smith et al., 2022), as these coordinated model experiments will enable the impacts of different external drivers to be isolated that otherwise would be buried under internal variability while also offering a testbed for methods to extract predictable signals with the correct amplitude.

This special issue (SI) aims to enhance our understanding of the complex, cascading interactions between natural hazards, health systems, disease outbreaks, and societal health. By compiling a high-quality collection of papers, we seek to

1. provide an overview of the state of the art for multi-hazards and health research;
2. showcase new research on the health impacts of disasters, particularly when they coincide with disease outbreaks;
3. advance modelling and measurement capabilities for multi-hazard scenarios involving public health emergencies;
4. identify synergies and trade-offs in disaster risk reduction (DRR) and adaptation strategies.

Natural hazard emergencies are fundamentally a complex interaction of natural, anthropogenic, and biological processes. For example, the COVID-19 pandemic highlighted the operational challenges of responding to events like the 2020 Zagreb earthquake amidst lockdowns and travel restrictions. Similarly, devastating floods in Pakistan in 2022 led to outbreaks of cholera and diarrhoea. These events demonstrate that a limited understanding of the cascading effects of combined disasters and diseases creates major operational, ethical, and decision-making challenges for disaster management, humanitarian, and development organizations. However, until relatively recently, there has been little engagement between the multi-hazard and health research communities to understand how these processes interact and feed off each other.

International frameworks, such as the United Nation's Sendai Framework for Disaster Risk Reduction (SFDRR) and the latest Intergovernmental Panel on Climate Change reports (Assessment Report 6 cycle), recognize the need to move beyond single-hazard thinking and address the complexities of multiple and systemic risks. The scientific community has been called upon to improve our understanding of these spatiotemporal complexities. The pre-print paper, titled Invited perspective: Redefining Disaster Risk: The Convergence of Natural Hazards and Health Crises by Sairam and De Ruiter in NHESS, for example, explores the interconnections between natural hazards, health, and society, highlighting the need for a more integrated approach.

While separate communities have advanced research on multi-hazard and systemic risks, there is a clear need to bring together a dedicated body of work on the unique intersection of disasters, diseases, health, and health systems. This SI provides that opportunity, fostering cross-disciplinary learning and identifying new research avenues. The urgency of this topic is underscored by the compounding effects of climate change on health systems and health outcomes, as well as the spatial and temporal variability of exposures and vulnerabilities to these complex hazards. This SI is part of the RiskKAN (https://www.risk-kan.org/) working group on the same topic.

The climate history since the Last Interglacial (~ 130 000 years ago) is marked by profound transitions, ranging from abrupt events to gradual reorganizations of the Earth system. This period offers a unique testing ground for evaluating and refining Earth system models across a wide spectrum of boundary conditions, from glacial extremes to interglacial warmth.

Simulating the climate dynamics of the last glacial cycle – including transitions, feedbacks, and tipping elements – enables us to assess the structural robustness of Earth system models used for future projections. Such model–data comparisons are critical not only to constrain uncertainties, but also to understand possible regime shifts in climate variability, the emergence of nonlinear behaviour, and the relevance of long-term feedbacks under anthropogenic forcing.

The purpose of this special issue is to provide a curated resource for warming-induced greenhouse gas emissions from both empirical and modelling studies to help understand their response to climate overshoot and their implications for climate stabilization.

The purpose of this special issue is to provide a curated resource for warming-induced greenhouse gas emissions from both empirical and modelling studies to help understand their response to climate overshoot and their implications for climate stabilization.

There is a crucial societal need to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth’s climate system, including capabilities for early warning of potential high-impact events and changes. To meet this need, the climate science community needs to provide actionable scientific information that meets the evolving needs of societies all over the world. This goal is shared by the World Climate Research Programme (WCRP) Lighthouse Activity on Explaining and Predicting Earth System Change (EPESC) and by the WCRP’s core project Atmospheric Processes and their Role in Climate (APARC) Large Ensembles for Attribution of Dynamically-driven ExtRemes (LEADER) activity.

In pursuit of this goal, this special issue welcomes research that addresses prediction, projection, and attribution of phenomena and events acting on annual to decadal timescales. This includes evaluating existing and developing new methodologies to attribute and predict changes and extremes driven by the atmospheric or oceanic circulation. We also seek advances that can lead to more reliable quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization and to improved projections.

While early warnings of changes in the Earth system are now potentially possible through, e.g., operational decadal predictions, there are several challenges: there is a lack of understanding of the dynamical mechanisms that enable such projections, and there is evidence that global models underestimate some predictable signals and that there are systematic discrepancies between simulated and observed trends. The special issue seeks research that highlights these challenges and improves our understanding of the causes of regional climate changes, needed both to attribute recent events and to gain further confidence in forecasts.

A particular focus of this special issue is papers that analyze output from the Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP; Smith et al., 2022), as these coordinated model experiments will enable the impacts of different external drivers to be isolated that otherwise would be buried under internal variability while also offering a testbed for methods to extract predictable signals with the correct amplitude.

This special issue (SI) aims to enhance our understanding of the complex, cascading interactions between natural hazards, health systems, disease outbreaks, and societal health. By compiling a high-quality collection of papers, we seek to

1. provide an overview of the state of the art for multi-hazards and health research;
2. showcase new research on the health impacts of disasters, particularly when they coincide with disease outbreaks;
3. advance modelling and measurement capabilities for multi-hazard scenarios involving public health emergencies;
4. identify synergies and trade-offs in disaster risk reduction (DRR) and adaptation strategies.

Natural hazard emergencies are fundamentally a complex interaction of natural, anthropogenic, and biological processes. For example, the COVID-19 pandemic highlighted the operational challenges of responding to events like the 2020 Zagreb earthquake amidst lockdowns and travel restrictions. Similarly, devastating floods in Pakistan in 2022 led to outbreaks of cholera and diarrhoea. These events demonstrate that a limited understanding of the cascading effects of combined disasters and diseases creates major operational, ethical, and decision-making challenges for disaster management, humanitarian, and development organizations. However, until relatively recently, there has been little engagement between the multi-hazard and health research communities to understand how these processes interact and feed off each other.

International frameworks, such as the United Nation's Sendai Framework for Disaster Risk Reduction (SFDRR) and the latest Intergovernmental Panel on Climate Change reports (Assessment Report 6 cycle), recognize the need to move beyond single-hazard thinking and address the complexities of multiple and systemic risks. The scientific community has been called upon to improve our understanding of these spatiotemporal complexities. The pre-print paper, titled Invited perspective: Redefining Disaster Risk: The Convergence of Natural Hazards and Health Crises by Sairam and De Ruiter in NHESS, for example, explores the interconnections between natural hazards, health, and society, highlighting the need for a more integrated approach.

While separate communities have advanced research on multi-hazard and systemic risks, there is a clear need to bring together a dedicated body of work on the unique intersection of disasters, diseases, health, and health systems. This SI provides that opportunity, fostering cross-disciplinary learning and identifying new research avenues. The urgency of this topic is underscored by the compounding effects of climate change on health systems and health outcomes, as well as the spatial and temporal variability of exposures and vulnerabilities to these complex hazards. This SI is part of the RiskKAN (https://www.risk-kan.org/) working group on the same topic.

The climate history since the Last Interglacial (~ 130 000 years ago) is marked by profound transitions, ranging from abrupt events to gradual reorganizations of the Earth system. This period offers a unique testing ground for evaluating and refining Earth system models across a wide spectrum of boundary conditions, from glacial extremes to interglacial warmth.

Simulating the climate dynamics of the last glacial cycle – including transitions, feedbacks, and tipping elements – enables us to assess the structural robustness of Earth system models used for future projections. Such model–data comparisons are critical not only to constrain uncertainties, but also to understand possible regime shifts in climate variability, the emergence of nonlinear behaviour, and the relevance of long-term feedbacks under anthropogenic forcing.