Atmospheric rivers (ARs) are now widely known for their association with high‐impact weather events and long‐term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR‐related metrics based on 20+ different AR identification and tracking methods applied to Modern‐Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria‐based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all‐method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR‐related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR‐related research to consider.

The performance of Statistical Downscaling (SD) techniques is critically re-assessed with respect to their robust applicability in climate change studies. To this aim, in addition to standard accuracy measures and distributional similarity scores, we estimate the robustness of the methods under warming climate conditions working with anomalous warm historical periods. This validation framework is applied to intercompare the performance of twelve different SD methods (from the analogs, weather typing and regression families) for downscaling minimum and maximum temperatures in Spain. First, we perform a calibration of these methods in terms of both geographical domains and predictor sets; the results are highly dependent on the latter, with optimum predictor sets including information of near-surface temperature (in particular 2 meters temperature), which discriminate appropriately cold episodes related to temperature inversion in the lower troposphere. Although regression methods perform best in terms of correlation, analog and weather generator approaches are more appropriate for reproducing the observed distributions, especially in case of wintertime minimum temperature. However, the latter two families significantly underestimate the temperature anomalies of the warm periods considered in this work. This underestimation is found to be critical when considering the warming signal in the late 21st century as given by a Global Climate Model (the ECHAM5-MPI model). In this case, the different downscaling methods provide warming values with differences in a range of 1 degC, in agreement with the robustness significance values. Therefore, the proposed test for robustness is a promising technique for detecting lack of robustness in statistical downscaling methods for climate change projections.

This study provides a comprehensive evaluation of seven Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 5 in present climate conditions from a downscaling perspective, taking into account the requirements of both statistical and dynamical approaches. ECMWF's ERA-Interim reanalysis is used as reference for an evaluation of circulation, temperature and humidity variables on daily timescale, which is based on distributional similarity scores. To additionally obtain an estimate of reanalysis uncertainty, ERA-Interim's deviation from the Japanese Meteorological Agency JRA-25 reanalysis is calculated. Areas with considerable differences between both reanalyses do not allow for a proper assessment, since ESM performance is sensitive to the choice of reanalysis.For use in statistical downscaling studies, ESM performance is computed on the grid-box scale and mapped over a large spatial domain covering Europe and Africa, additionally highlighting those regions where significant distributional differences remain even for the centered/zeromean time series. For use in dynamical downscaling studies, performance is specifically assessed along the

Fire is an integral Earth system process, playing an important role in the distribution of terrestrial ecosystems and affecting the carbon cycle at the global scale. Fire activity is controlled by a number of biophysical factors, including climate, whose relevance varies across regions and landscapes. In light of the ongoing climate change, understanding the fire-climate relationships is an issue of current interest in order to identify the most vulnerable regions. Building upon recent global observations of burned areas and climate, we investigate the sensitivity of fire activity to fire-weather across the world's major biomes. We identify the biomes susceptible to inter-annual fire-weather fluctuations, unveiling a non-linear relationship with a saturation threshold past which the area burned can be considered insensitive to increasing fire-weather. Our results depict an unambiguous spatial pattern that identifies the world regions where short-term climate fluctuations are