John W. Nielsen-Gammon

Urbanization modifies precipitation ^1,2 , yet previous studies have reported inconsistent results, with some cities experiencing rainfall enhancement and others showing suppression ³ . To reconcile these discrepancies, we examine how urban impacts vary across storm types using an event-based analysis. With three-dimensional radar reflectivity data (1995–2017), we identify more than 40,000 warm-season storms across four Texas cities (Dallas, Austin, San Antonio and Houston). Here we show that classifying storms into five types reveals distinct urban influences linked to storm scales and dynamics. Local-scale single-cell and isolated storms, driven by atmospheric instability, increase in frequency (7–31%), particularly at night. Synoptic-scale frontal storms show unchanged occurrence but contrasting intensity responses: cold fronts weaken over cities by 16–28%, probably because of thermal and roughness effects, whereas warm fronts exhibit enhanced reflectivity. Tropical systems show no consistent change in frequency or intensity but exhibit a shift of high-reflectivity grid cells towards lower altitudes over urban areas. Given the diverse climate and geography of Texas, this work provides a transferable framework for understanding urban–storm interactions in other regions. These findings move beyond the traditional ‘urban wet or dry islands’ model, advancing our understanding of how urbanization modulates extreme precipitation and informing climate modelling ^4,5 and resilience planning for rapidly growing cities ^6,7 .

Cities and urban areas present important opportunities for advancing toward sustainability and resilience goals. From a systems perspective, various targets and indicators of key outcomes reflect sustainability of urban areas, motivating consideration of local and regional context. We explore four key themes for creating sustainable urban areas: 1) urban areas are complex systems linked with diverse rural areas across the urban-rural gradient, 2) data and modeling of physical and non-physical systems can aid decision making for sustainability, 3) creating sustainable urban-rural systems requires collaboration with partner communities, and 4) change requires adaptive and transformative governance frameworks. These themes guide sustainability innovations through co-production of knowledge among communities, authorities, managers, and researchers. The sustainability of urban areas strongly depends on urban-rural connectivity regarding both physical systems and governance frameworks.

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community‐driven health interventions. The inaugural One Earth , One Health workshop sought to explore this gap through human‐centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co‐develop solutions with affected communities, they used stakeholder role‐playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context‐aware guidance beyond one‐size‐fits‐all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real‐time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process‐driven, human‐centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

The state of Texas is large and geographically complex. Its climate is determined by its geographical setting a third of the way from the Equator to the North Pole, bounded on its west by continent-scale mountains and on its southeast by the Gulf of Mexico, with a vast expanse of land stretching from its northern border to the Arctic Ocean. Precipitation generally increases from west to east, while temperature generally increases from north to south. Tornadoes are most likely in April and May, while hurricanes are a danger from June through September. As expected, temperatures have been increasing faster in Texas than the global average. Precipitation has trended upward in central and eastern Texas, but it is not clear if that will continue. Future changes in Texas climate will depend on future atmospheric greenhouse gas concentrations, the sensitivity of the climate to those gases, and the impacts of global climate changes on Texas weather patterns, as well as other factors such as natural variability and urbanization. While temperatures are expected to continue rising, future changes in extreme weather in Texas, such as with droughts, hurricanes, and wildfires, are multifaceted, but with a generally increased risk of the most extreme events.

Uncertainty is inherent to all sciences and can be studied from several different perspectives. However, best practices for climate scientists communicating uncertainty in climate projections are unclear. As anthropogenic greenhouse gas emissions continue to rise, the impacts of human activity on the climate system have become more apparent. This makes the communication of uncertainty in the climate projections critical to decision-makers. Further, the public often equates science to certainty. Yet, it is critical to understand that climate projections are not the future and that projections themselves contain several sources of uncertainty. As such, this review makes four primary recommendations to guide future research and considerations when communicating uncertainty. The goal is to provide a central place for climate scientists to have crucial conversations on how to communicate this uncertainty to facilitate decision-making. First, a standardized uncertainty communication framework, specific to climate projections, should be developed and implemented. Second, research is needed to determine how to communicate which climate projections are best representing current reality. This is critical given recent funding uncertainties that could impact the ability to make more certain climate projections. Third, there is a lack of research on how different decision-makers perceive uncertainty, which could point to a need to develop industry-specific uncertainty communication practices when developing a larger, universal uncertainty communication framework. Finally, we recommend investigating whether too much emphasis is placed on the potential impacts of climate change (negative framing of uncertainty) rather than on actions the public can take to reduce the projected warming (positive framing). Significance Statement: Communicating uncertainty in the climate projections is crucial to informing and enhancing decision-making processes in sectors that are exceptionally impacted by changes in the climate (e.g., water resource managers, farmers, insurance companies, etc.). Yet, best practices for communicating climate projection uncertainty are not universal and unclear. The goal for this review is to help facilitate critical conversations around the need to standardize climate projection uncertainty communication. This review makes four primary recommendations to guide future research efforts.

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human centered design exercises involving interdisciplinary researchers from climate and earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change without getting into contentious debate about climate change. Participants focused on three examples---infectious disease, extreme weather, and air quality---while recognizing these do not encompass all health threats. Key solutions included advanced strategies for predicting infectious disease outbreaks, inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to make tailored public health recommendations. The workshop highlighted opportunities such as leveraging artificial intelligence and real-time epidemiological surveillance to protect communities, but also noted barriers including data quality, technological redundancy, and limitations. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. The suite of proposed strategies highlights the importance of cross-sector collaboration, interdisciplinary training, and direct community involvement in co-creating content-specific, inclusive responses for mitigating health threats amplified by global environmental change.

Drought is a period of abnormally dry weather that leads to hydrological imbalance. Drought assessments determine the characteristics, severity, and impacts of a drought. Climate change adds conceptual and quantitative challenges to traditional drought assessments. This paper highlights the challenges of assessing drought in a climate made non‐stationary by human activities or natural variability. To address these challenges, we then identify 10 key research priorities for advancing drought science and improving assessments in a changing climate. The priorities focus on improving drought indicators to account for non‐stationarity, evaluating drought impacts and their trends, addressing regional differences in non‐stationarity, determining the physical drivers of drought and how they are changing, capturing precipitation variability, and understanding the drivers of aridification. Ultimately, improved drought assessments will inform better risk management, adaptation strategies, and planning, especially in areas where climate change significantly alters drought dynamics. This perspective offers a path toward more accurate and effective drought management in a non‐stationary climate system.

Climate has a profound influence on the most fundamental properties of Texas bays and estuaries: their water temperatures and salinities, their circulation, and even their locations and spatial extents. With climate change, these influences will evolve and possibly change the character of bays and estuaries. Key climate changes include warmer temperatures, changes in amount and distribution of precipitation, and sea level rise. Inflows to Texas bays and estuaries are not altered solely by climate change, but it is possible to infer the impacts attributable to climate change. Specifically, hydrologic model simulations driven by downscaled climate model projections tend to predict roughly equal chances of increases or decreases in total inflows, but with a clear tendency for more erratic inflows as the wettest months get wetter and the driest months get drier. Projections of most hydrological variables have largely spread across models, indicating that change is likely, but the specific magnitude of change cannot be discerned at this time. Both increasing variability and a long-term trend imply that estuarine ecosystems must be able to adapt to a broader range of conditions than those found historically. Management decisions should allow for a range of possible future climate impacts.

Unprecedented precipitation deficits in the 2022–2023 growing season across the primary wheat-producing region in the United States caused delays in winter wheat emergence and poor crop growth. Using an integrated approach, we quantitatively unraveled a 37% reduction in wheat production as being attributable to both per–harvested acre yield loss and severe crop abandonment, reminiscent of the Dust Bowl years in the 1930s. We used random forest machine learning and game theory analytics to show that the main driver of yield loss was spring drought, whereas fall drought dominated abandonment rates. Furthermore, results revealed, across the US winter wheat belt, the La Niña phase of the El Niño Southern Oscillation (ENSO), increased abandonment rates compared to the El Niño phase. These findings underscore the necessity of simultaneously addressing crop abandonment and yield decline to stabilize wheat production amid extreme climatic conditions and provide a holistic understanding of global-scale ENSO dynamics on wheat production.

This study estimates extreme rainfall trends across the Gulf Coast and southeastern coast of the United States while applying methods for extending the temporal record and aggregating across spatial trend variations. Nonstationary generalized extreme value (GEV) models are applied to historical annual daily maximum precipitation data (1890–2019) while using CMIP5 global mean surface temperature (GMST) as the covariate. County composites and multicounty regions are used for local data record extension and pooling. Unlike most previous studies, return periods as long as 100 years are analyzed. The local trend estimates themselves are found to be too noisy to be reliable as estimates of climate-driven trends. However, application of a Gaussian process model to the spatial distribution of observed trends yields overall trend detection at the 95% significance level. The overall historical increase due to nonstationarity across the study region, with associated 95% confidence intervals, is 9% (3%, 15%) for the 2-yr return period and 16% (4%, 26%) for the 100-yr return period. A trend is also detectable in the Gulf Coast subregion, but not in the smaller southeast subregion. Recent weather events and nonstationarity have caused the official return value estimates for parts of North and South Carolina to be much lower than the return values estimated here. Significance Statement: Protection of people and infrastructure from flooding relies on accurate estimates of potential extreme rainfall intensity. Some official estimates of extreme rainfall near the Gulf Coast and southeastern coast of the United States are over 20 years old. We show that, across this region, there is a clear trend in daily rainfall so extreme that it only has a 1% chance of happening in any given year (the so-called 100-yr rainfall). This trend means that many existing estimates of extreme rainfall are too low, both now and in the future, so flooding risks based on those estimates would be underestimated as well.