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The consecutive occurrence of meteorological dry and wet extremes has been receiving increasing attention due to potentially larger social and environmental impacts than single extremes. However, the changing characteristics of transitions between dry and wet periods remain poorly understood. Here we investigate the dynamic evolution of dry‐to‐wet transitions in response to climate warming, using the Standardized Precipitation Evapotranspiration Index as well as observations and general circulation models for the periods of 1954–2014 and 2040–2100. We find that approximately three‐fifths of global land area are projected to experience an accelerated dry‐to‐wet transition. Southern Asia has been experiencing the most severe dry‐to‐wet transitions and is projected to suffer more frequent, more intense, and accelerated dry‐to‐wet transitions. The increasing potential evapotranspiration (PET) variability plays an important role in accelerating the dry‐to‐wet transition in Southern Asia, while precipitation and PET variabilities contribute to the intensification of dry‐to‐wet transitions in Southern North America. Plain Language Summary: The consecutive occurrence of droughts and floods has widespread impacts on agriculture, the ecosystem, and the environment. Here we examine the changes in frequency, intensity, and elapsed time of transitions between dry and wet periods under a warming climate, based on observations and an ensemble of 10 Coupled Model Intercomparison Project Phase 6 climate simulations. We find that approximately 59% of global land area are expected to experience a shorter transition time between dry and wet periods. Southern Asia suffers the most severe dry‐to‐wet transition. Furthermore, our findings reveal that enhanced potential evapotranspiration (PET) variability contributes to the accelerated transition between dry and wet periods over Southern Asia, and the acceleration of dry‐to‐wet transitions in Southern North America is associated with an increase in precipitation and PET variabilities as climate warms. Key Points: Approximately three‐fifths (59%) of global land area are projected to experience accelerated transition between dry and wet periodsSouthern Asia suffers the most from severe transition between dry and wet periodsEnhanced precipitation and potential evapotranspiration variabilities contribute to the accelerated dry‐to‐wet transitions [ABSTRACT FROM AUTHOR]

The compound of late winter snow droughts and early spring heatwaves (compound snow drought and heatwave (CSDHW)) could dramatically affect ecosystems and water availability, but has not been systematically investigated. Here we present a comprehensive assessment of CSDHW events and possible driving mechanisms. We find that 7% of the snow‐covered area experiences significant (p < 0.05) CSDHW events, and an average of 35% of snow droughts are followed by heatwaves during 1981–2020. The spatial extent of CSDHW is asymmetrically enlarging, with a significant increase in Eurasia and a relatively high fluctuation in North America. Specifically, the warm‐type CSDHW (i.e., snow drought with normal or above‐average precipitation followed by heatwave) occurs more frequently, with spatial coverage increasing faster than the dry‐type CSDHW (i.e., snow drought with below‐average precipitation followed by heatwave). In comparison, dry snow drought is more likely to be followed by heatwave due to intensified soil drought and atmospheric aridity. Plain Language Summary: Snow drought and heatwave events have adverse impacts on society and ecosystems, and have drawn much attention in the past decades. However, the consecutive occurrence of the two extreme events remains poorly understood. This study presents a global assessment of compound snow drought and heatwave (CSDHW) events for the period from 1981 to 2020. We find that more than one third (35%) of snow droughts are followed by heatwaves. The spatial extent of CSDHW is expanding globally, with a significant increase in Eurasia and a relatively high fluctuation in North America. Despite the higher frequency and expanding trend of the warm‐type CSDHW (i.e., snow drought with normal or above‐average precipitation followed by heatwave) than the dry‐type CSDHW (i.e., snow drought with below‐average precipitation followed by heatwave), dry snow drought is more likely to be followed by heatwaves, which can be attributed to intensifying soil drought and atmospheric aridity after dry snow drought. Key Points: There is a significant spatial expansion (13%) of compound snow drought and heatwave (CSDHW) events in the snow‐covered area of EurasiaThe warm‐type CSDHW becomes more frequent and expands faster than the dry‐type CSDHWDry snow drought promotes the occurrence of subsequent heatwave associated with intensified soil drought and atmospheric aridity [ABSTRACT FROM AUTHOR]

A novel strategy for the rapid detection and identification of traditional and emerging Campylobacter strains based upon Raman spectroscopy (532 nm) is presented here. A total of 200 reference strains and clinical isolates of 11 different Campylobacter species recovered from infected animals and humans from China and North America were used to establish a global Raman spectroscopy-based dendrogram model for Campylobacter identification to the species level and cross validated for its feasibility to predict Campylobacter-associated food-borne outbreaks. Bayesian probability coupled with Monte Carlo estimation was employed to validate the established Raman classification model on the basis of the selected principal components, mainly protein secondary structures, on the Campylobacter cell membrane. This Raman spectroscopy-based typing technique correlates well with multilocus sequence typing and has an average recognition rate of 97.21%. Discriminatory power for the Raman classification model had a Simpson index of diversity of 0.968. Intra- and interlaboratory reproducibility with different instrumentation yielded differentiation index values of 4.79 to 6.03 for wave numbers between 1,800 and 650 cm(-1) and demonstrated the feasibility of using this spectroscopic method at different laboratories. Our Raman spectroscopy-based partial least-squares regression model could precisely discriminate and quantify the actual concentration of a specific Campylobacter strain in a bacterial mixture (regression coefficient, >0.98; residual prediction deviation, >7.88). A standard protocol for sample preparation, spectral collection, model validation, and data analyses was established for the Raman spectroscopic technique. Raman spectroscopy may have advantages over traditional genotyping methods for bacterial epidemiology, such as detection speed and accuracy of identification to the species level.