The intersection of atmospheric pollution, climate change, and public health presents an urgent global challenge. As climatic shifts alter atmospheric dynamics, pollutant behavior, and exposure patterns, there is a pressing need to reassess traditional approaches to health risk evaluation. New sources of pollution, changing chemical transformations in the atmosphere, and emerging vulnerabilities in human populations demand innovative research and adaptive solutions.

This Collection seeks contributions that explore the complex relationships between atmospheric pollution, climate variability, and health impacts. We invite original research articles, reviews, and case studies that advance scientific understanding, methodological approaches, and policy frameworks for assessing and managing health risks in a changing environmental context.

Key Themes and Topics Include (but are not limited to):

Climate-induced variations in pollutant exposure and health outcomes

Novel methodologies for integrated air pollution and climate-related health risk assessments

Impacts of extreme weather events (e.g., wildfires, heatwaves) on air quality and human health

Modeling and prediction of air pollution-health interactions under future climate scenarios

Innovations in air pollution monitoring and exposure assessment in a changing climate

Vulnerability mapping and public health preparedness strategies

Policy interventions addressing the nexus of air quality, climate change, and public health

Co-benefits of integrated mitigation strategies targeting air pollution and climate adaptation

Emerging pollutants (e.g., secondary organic aerosols, microplastics) and their health risks

Socioeconomic and environmental justice aspects of climate-related air pollution impacts

This Collection aims to provide a platform for interdisciplinary research bridging atmospheric science, climate studies, public health, and policy innovation, fostering new solutions to reduce health risks in an era of rapid environmental change.

Keywords: Climate Change And Health; Risk Factors; Ecotoxicology; Toxicology; Cancer; Urban area

Precipitation is a fundamental component of the Earth's water cycle, influencing weather patterns, climate variability, ecosystems, agriculture, and water resource management. Understanding the underlying processes of precipitation formation, distribution, and intensity is crucial for addressing challenges such as extreme weather events, flood forecasting, drought mitigation, and climate change adaptation. Traditional approaches relying on observational data and numerical modeling have provided significant insights, yet persistent uncertainties remain due to the complex interactions between atmospheric dynamics, thermodynamics, and microphysical processes.

In recent years, advancements in machine learning (ML) and deep learning (DL) techniques have opened new avenues for enhancing our understanding of precipitation processes. These data-driven methods can integrate vast datasets from satellite observations, ground-based radars, weather stations, and high-resolution models to uncover patterns, improve predictions, and refine parameterizations that are difficult to capture through conventional physics-based approaches alone. This Special Issue aims to bring together cutting-edge research that bridges observational studies, numerical modeling, and ML/DL methodologies to advance the science of precipitation. We seek contributions that explore innovative integrations of these approaches to better characterize precipitation mechanisms, from cloud microphysics to large-scale atmospheric circulation.

By fostering interdisciplinary collaboration, this collection will highlight novel techniques, datasets, and findings that contribute to more accurate forecasting, risk assessment, and policy-making in a changing climate.

We welcome original research articles, review papers, case studies, and methodological advancements related to the following themes (but not limited to):

• Observational analyses of precipitation processes using remote sensing, in-situ measurements, or reanalysis datasets.

• Numerical modeling of precipitation, including improvements in parameterization schemes for convection, cloud-resolving models, and ensemble simulations.

• Applications of ML/DL for precipitation nowcasting, downscaling, bias correction, or pattern recognition in observational and modeled data.

• Hybrid approaches combining physics-based models with ML/DL for enhanced simulation and prediction of extreme precipitation events.

• Studies on the impacts of climate change on precipitation processes using integrated observational-modeling-ML frameworks.

• Validation and uncertainty quantification in precipitation studies across scales, from local to global.

• Societal and environmental implications, such as hydrological modeling, disaster risk reduction, and water resource planning informed by these approaches.