Regional climate models are important research tools available to scientists around the world, including in economically developing nations (EDNs). The Earth Systems Physics (ESP) group of the Abdus Salam International Centre for Theoretical Physics (ICTP) maintains and distributes a state-of-the-science regional climate model called the ICTP Regional Climate Model version 3 (RegCM3), which is currently being used by a large research community for a diverse range of climate-related studies. The RegCM3 is the central, but not only, tool of the ICTP-maintained Regional Climate Research Network (RegCNET) aimed at creating south–south and north–south scientific interactions on the topic of climate and associated impacts research and modeling. In this paper, RegCNET, RegCM3, and illustrative results from RegCM3 benchmark simulations applied over south Asia, Africa, and South America are presented. It is shown that RegCM3 performs reasonably well over these regions and is therefore useful for climate studies in EDNs.

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).

The existence of several gridded precipitation products (GPP) has facilitated studies related to climate change, climate modeling, as well as a better understanding of the physical processes underpinning this key variable. Due to complexities in estimating rainfall, gridded datasets exhibit different levels of accuracy across regions, even when they are developed at relatively high resolution or using sophisticated procedures. The performance of 16 GPP are evaluated over the Caribbean region, which includes the Caribbean Islands, and portions of Central South America. Monthly data for sixty weather stations are used as a reference for the period 1983–2010. The 16 GPP include six products based on station data only, two that combine ground station and satellite information, two merging station and reanalysis information, four based on reanalysis, and two using multisource information. The temporal resolution of the GPP ranged between daily and monthly and spatial resolution from 0.033° to 0.5°. The methodological approach employed combined a comparison of regional and sub-regional precipitation annual cycles, the Kling–Gupta efficiency (KGE) index, as well as several metrics derived from the standardized precipitation index (SPI). Overall, the best performances were obtained from GPCC025 and MSWEP2, likely reflecting the positive impact of the large number of station data utilized in their development. It is also demonstrated that a higher spatial resolution does not always mean better accuracy. There is a need for this kind of assessment when undertaking climate studies in regions like the Caribbean where resolution is a significant consideration. ERA5 performed best among the reanalyses analyzed and has the potential to be used to develop regionally based GPP by applying bias correction or downscaling techniques. The methodological approach employed provides a comprehensive and robust evaluation of the relative strengths and weaknesses of GPP in the Caribbean region.

The ability of the WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) model to forecast extreme rainfall in the Central Andes of Peru is evaluated in this study, using observations from stations located in the Mantaro basin and GOES (Geostationary Operational Environmental Satellite) images. The evaluation analyzes the synoptic conditions averaged over 40 extreme event cases, and considers model simulations organized in 4 nested domains. We first establish that atypical events in the region are those with more than 27 mm of rainfall per day when averaging over all the stations. More than 50% of the selected cases occurred during January, February, and April, with the most extreme occurring during February. The average synoptic conditions show negative geopotential anomalies and positive humidity anomalies in 700 and 500 hPa. At 200 hPa, the subtropical upper ridge or “Bolivian high” was present, with its northern divergent flank over the Mantaro basin. Simulation results show that the Weather Research and Forecasting (WRF) model underestimates rainfall totals in approximately 50–60% of cases, mainly in the south of the basin and in the extreme west along the mountain range. The analysis of two case studies shows that the underestimation by the model is probably due to three reasons: inability to generate convection in the upstream Amazon during early morning hours, apparently related to processes of larger scales; limitations on describing mesoscale processes that lead to vertical movements capable of producing extreme rainfall; and limitations on the microphysics scheme to generate heavy rainfall.