Abstract. Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset. We present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages). We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: https://github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), https://doi.org/10.5281/zenodo.3998463 (Iturbide et al., 2020).

Regional Climate Models (RCMs) constitute the most often used method to perform affordable highresolution regional climate simulations. The key issue in the evaluation of nested regional models is to determine whether RCM simulations improve the representation of climatic statistics compared to the driving data, that is, whether RCMs add value. In this study we examine a necessary condition that some climate statistics derived from the precipitation field must satisfy in order that the RCM technique can generate some added value: we focus on whether the climate statistics of interest contain some fine spatial-scale variability that would be absent on a coarser grid. The presence and magnitude of fine-scale precipitation variance required to adequately describe a given climate statistics will then be used to quantify the potential added value (PAV) of RCMs. Our results show that the PAV of RCMs is much higher for short temporal scales (e.g., 3-hourly data) than for long temporal scales (16-day average data) due to the filtering resulting from the time-averaging process. PAV is higher in warm season compared to cold season due to the higher proportion of precipitation falling from small-scale weather systems in the warm season. In regions of complex topography, the orographic forcing induces an extra component of PAV, no matter the season or the temporal scale considered. The PAV is also estimated using high-resolution datasets based on observations allowing the evaluation of the sensitivity of changing resolution in the real climate system. The results show that RCMs tend to reproduce relatively well the PAV compared to observations although showing an overestimation of the PAV in warm season and mountainous regions.

Compound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs.

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This paper summarises the current state of understanding with respect to the added value (AV) to be expected from one-way nested high-resolution regional climate simulations and projections. The reasons that lead to the development and the progress of regional climate models (RCMs) are first considered. The scientific basis sustaining the RCMs mission is then briefly reviewed. Based on recent publications of studies on the topic of AV, concepts related to the various definitions of AV are examined with the aim of clarifying their meaning and of bridging different schools of thought. The conditions under which AV can be expected, and in which variables and statistical moments, are also discussed. Keywords Regional climate model • Dynamical downscaling • Added value • Climate change signal • Evaluation • Boundary conditions This article is part of the Topical Collection on Advances in Modeling

We examine the joint contribution of urban expansion and climate change on heat stress over the Sydney region. A Regional Climate Model was used to downscale present (1990–2009) and future (2040–2059) simulations from a Global Climate Model. The effects of urban surfaces on local temperature and vapor pressure were included. The role of urban expansion in modulating the climate change signal at local scales was investigated using a human heat-stress index combining temperature and vapor pressure. Urban expansion and climate change leads to increased risk of heat-stress conditions in the Sydney region, with substantially more frequent adverse conditions in urban areas. Impacts are particularly obvious in extreme values; daytime heat-stress impacts are more noticeable in the higher percentiles than in the mean values and the impact at night is more obvious in the lower percentiles than in the mean. Urban expansion enhances heat-stress increases due to climate change at night, but partly compensates its effects during the day. These differences are due to a stronger contribution from vapor pressure deficit during the day and from temperature increases during the night induced by urban surfaces. Our results highlight the inappropriateness of assessing human comfort determined using temperature changes alone and point to the likelihood that impacts of climate change assessed using models that lack urban surfaces probably underestimate future changes in terms of human comfort.

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This study evaluates the added value in the representation of surface climate variables from an ensemble of regional climate model (RCM) simulations by comparing the relative skill of the RCM simulations and their driving data over a wide range of RCM experimental setups and climate statistics. The methodology is specifically designed to compare results across different variables and metrics, and it incorporates a rigorous approach to separate the added value occurring at different spatial scales. Results show that the RCMs' added value strongly depends on the type of driving data, the climate variable, and the region of interest but depends rather weakly on the choice of the statistical measure, the season, and the RCM physical configuration. Decomposing climate statistics according to different spatial scales shows that improvements are coming from the small scales when considering the representation of spatial patterns, but from the large-scale contribution in the case of absolute values. Our results also show that a large part of the added value can be attained using some simple postprocessing methods.