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Abstract Objective: To synthesize the methodologies of studies that evaluate the impacts of heat exposure on morbidity and mortality. Methods: Embase, MEDLINE, Web of Science, and Scopus were searched from date of inception until 1 March 2023 for English language literature on heat exposure and health outcomes. Records were collated, deduplicated and screened, and full texts were reviewed for inclusion and data abstraction. Eligibility for inclusion was determined as any article with climate‐related heat exposure and an associated morbidity/mortality outcome. Results: Of 13,136 records initially identified, 237 articles were selected for analysis. The scope of research represented 43 countries, with most studies conducted in China (62), the USA (44), and Australia (16). Across all studies, there were 141 unique climate data sources, no standard threshold for extreme heat, and 200 unique health outcome data sources. The distributed lag non‐linear model (DLNM) was the most common analytic method (48.1% of studies) and had high usage rates in China (68.9%) and the USA (31.8%); Australia frequently used conditional logistic regression (50%). Conditional logistic regression was most prevalent in case‐control studies (5 of 8 studies, 62.5%) and in case‐crossover studies (29 of 70, 41.4%). DLNMs were most common in time series studies (64 of 111, 57.7%) and ecological studies (13 of 20, 65.0%). Conclusions: This review underscores the heterogeneity of methods in heat impact studies across diverse settings and provides a resource for future researchers. Underrepresentation of certain countries, health outcomes, and limited data access were identified as potential barriers.

Abstract Characterizing changes in precipitation patterns over time is critical for hydrologically dependent fields like water resource management and agriculture. Here, we explore observed trends in interannual precipitation variability using a suite of metrics that describe changes in precipitation over time. We analyze daily in situ Global Historical Climatology Network precipitation data from 1970 to present over 17 internally consistent sub‐national United States domains using a regional Mann‐Kendall trend test. We find robustly increasing trends in annual mean precipitation and wet day frequency for most of the central and eastern U.S., but decreasing trends in the western U.S. Importantly, we identify widespread significant trends in interannual precipitation variability, with increasing variability in the southeast, decreasing variability in the far west, and mixed signals in the Rocky Mountains and north‐central U.S. Our results provide important context for water resource managers and a new observational standard for climate model performance assessments.

Abstract Malaria is among the greatest public health threats in Mozambique, with over 10 million cases reported annually since 2018. Although the relationship between seasonal trends in environmental parameters and malaria cases is well established, the role of climate in deviations from the annual cycle is less clear. To investigate this and the potential for leveraging inter‐annual climate variability to predict malaria outbreaks, weekly district‐level malaria incidence spanning 2010–2017 were processed for a cross‐analysis with climate data. An empirical orthogonal function analysis of district‐level malaria incidence revealed two dominant spatiotemporal modes that collectively account for 81% of the inter‐annual variability of malaria: a mode dominated by variance over the southern half of Mozambique (64%), and another dominated by variance in the northern third of the country (17%). These modes of malaria variability are shown to be closely related to precipitation. Linear regression of global sea surface temperatures onto local precipitation indices over these variance maxima links the leading mode of inter‐annual malarial variability to the El Niño‐Southern Oscillation, such that La Niña leads to wetter conditions over southern Mozambique and, therefore, higher malaria prevalence. Similar analysis of spatiotemporal patterns of precipitation over a longer time period (1979–2019) indicate that the Subtropical Indian Ocean Dipole is both a strong predictor of regional precipitation and the climatic mechanism underlying the second mode of malarial variability. These results suggest that skillful malaria early warning systems may be developed that leverage quasi‐predictable modes of inter‐annual climate variability in the tropical oceans.

Abstract While the impact of climate on regional geopolitical stability and large‐scale conflict has garnered increased visibility in recent years, the effects of climate variability on interpersonal violent crime have received only limited scientific attention. Though earlier studies have established a modest correlation between temperature and violent crime, the underlying seasonality in both variables was often not controlled for and spatial heterogeneity of the statistical relationships has largely been overlooked. Here a method of spatial aggregation is applied to the United States, enabling a systematic investigation into the observed relationships between large‐scale climate variability and regionally aggregated crime rates. This novel approach allows for differentiation between the effects of two previously proposed mechanisms linking climate and violent crime, the Routine Activities Theory and Temperature‐Aggression Hypothesis. Results indicate large and statistically significant positive correlations between the interannual variability of wintertime air temperature and both violent and property crime rates, with negligible correlations emerging from summertime data. Results strongly support the Routine Activities Theory linking climate and violent crime, with climate variability explaining well over a third of the variance of wintertime violent crime in several broad regions of the United States. Finally, results motivate the development of observationally constrained empirical models and their potential application to seasonal and potentially longer‐term forecasts.

Background: Malaria is one of the greatest recurring threats to public health in Mozambique with approximately 10 million cases and thousands of deaths reported annually. Although a malaria early warning system is being established in the country, it is focused on short-term (4–8 week) prediction windows. Increased understanding of the links between quasi-predictable interannual climate variability and malaria could lengthen the lead time for the malaria early warning system and enhance planning and actionable mitigation efforts by public health officials in the country. Methods: To identify patterns of interannual variability, we did two principal component analyses of processed weekly district-level malaria incidences for the period 2010–17 in Mozambique and southern Africa regional precipitation for the period 1981–2019. We also did linear regression analyses of sea surface temperatures onto a precipitation index, and composites of various climate variables to establish links between precipitation and modes of climate variability. Findings: Two dominant spatiotemporal patterns collectively account for 81% of the interannual variability of malaria in Mozambique. These patterns consisted of a primary hotspot over the central and southern part of the country and a secondary hotspot over the northern third of the country. We found these patterns to be closely related to precipitation variability driven by climate phenomena: the El Niño-Southern Oscillation and the Subtropical Indian Ocean Dipole. Interpretation: Quasi-predictable global and regional climate phenomena influence regional precipitation over southern Africa. Knowledge of these associations can inform an enhancement of the malaria early warning system in Mozambique by lengthening early warning lead times. Similar analyses are possible for other countries whose climate is dominated by tropical modes of climate variability. Funding: None.