Atmospheric rivers (ARs) are narrow regions responsible for the majority of the poleward water vapor transport across the midlatitudes. They are characterized by high water vapor content and strong low level winds, and form a part of the broader warm conveyor belt of extratropical cyclones. Although the meridional water vapor transport within ARs is critical for water resources, ARs can also cause disastrous floods especially when encountering mountainous terrain. They were labeled as atmospheric rivers in the 1990s, and have since become a well-studied feature of the midlatitude climate. We briefly review the conceptual model, the methods used to identify them, their main climatological characteristics, their impacts, the predictive ability of numerical weather prediction models, their relationship with large-scale ocean-atmosphere dynamics, possible changes under future climates, and some future challenges.

We review the major conceptual models of atmospheric moisture transport, which describe the link between evaporation from the ocean and precipitation over the continents. We begin by summarizing some of the basic aspects of the structure and geographical distribution of the two major mechanisms of atmospheric moisture transport, namely low-level jets (LLJs) and atmospheric rivers (ARs). We then focus on a regional analysis of the role of these mechanisms in extreme precipitation events with particular attention to the intensification (or reduction) of moisture transport and the outcome, in terms of precipitation anomalies and subsequent flooding (drought), and consider changes in the position and occurrence of LLJs and ARs with respect to any associated flooding or drought. We then conclude with a graphical summary of the impacts of precipitation extremes, highlighting the usefulness of this information to hydrologists and policymakers, and describe some future research challenges including the effects of possible changes to ARs and LLJs within the context of future warmer climates.

Average monthly precipitation, the original Palmer Drought Severity Index (PDSI) and a recent adaptation to Europe, the Self Calibrated PDSI (scPDSI) have been used here to analyse the spatial and temporal evolution of drought conditions in the Mediterranean during the 20th century. Monthly, seasonal and annual trends were computed for the period 1901–2000 and also for the first and second halves of this period. The statistical significance of trends was obtained with a modified version of the Mann-Kendall test that accounts for serial auto-correlation. The results show a clear trend towards drier conditions during the 20th century in most western and central Mediterranean regions, with the exceptions of northwestern Iberia and most of Turkey that reveal an increase of moisture availability. A Generalized Extreme Values (GEV) analysis was applied to the maximum and minimum regional values of scPDSI, with results pointing towards a significant decline of absolute extreme values in central areas (Italy and Balkans) and a less clear picture emerging in western (Iberia) and eastern (Turkey) realms. The inter-annual variability of the scPDSI index series is shown to be more realistic than the corresponding PDSI version, fitting better the drought episodes sequence and magnitude described in the literature for each sub-region. We assess the decadal and inter-annual variability of the scPDSI for each sub-domain and evaluate the role played by the major teleconnection patterns, and by several sea surface temperature (SST) anomalies. The main driver of scPDSI in western and central Mediterranean areas is the winter North Atlantic Oscillation (NAO) pattern that is also relevant during the following spring and summer seasons with anti-correlation values below −0.60. The second most important mode corresponds to the Scandinavian Pattern that is significantly associated to the scPDSI between winter and summer over central Mediterranean (correlation values around 0.50). Finally, the teleconnection and SST analysis has allowed us to calibrate a stepwise regression model, enabling the forecasting of summer drought conditions six months in advance. The final model obtained is capable of reproducing the observed scPDSI time series fairly well, with a correlation coefficient of 0.79 (0.77 after cross-validation) and a significant gain over climatology (SS_c=59%), while the corresponding result against persistence is more modest (SS_p6=11%).