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A characterization of the diurnal cycle of precipitation (DCP) over the whole South America is still lacking in the literature and the scarcity of sub‐daily rain ground measurements limits the data available for analysis and forbids a correct validation of alternative datasets. In this paper we analyse the climatological mean DCP during the monsoon active season using five gridded datasets: two satellite precipitation estimates, two regional climate models and one reanalysis. Amazonia, the Brazilian Highlands, the northeastern South American coast, the Andes and the western Colombian coast are identified as the areas with most prominent DCPs. The afternoon convection triggered by solar heating over land and the coastal and topographic effects are the main modes of sub‐daily variability, based on an EOF decomposition of the 3 hourly mean precipitation fields. We explore the contribution of mean frequency and intensity to amount of precipitation and nighttime–daytime differences. In general, both models precipitate earlier and more frequently than the satellite products and do not reproduce correctly areas of observed predominant nighttime precipitation where mesoscale convective systems are active, like La Plata Basin or Amazonia. Over the analysed areas, the high frequency of precipitation is the driving mechanism of total amount in the models, whereas in the satellite products there is also considerable contribution from intensity. Overall, the reanalysis shows features in between the models and the satellite estimates, sharing characteristics with both types of data. The results presented here point at the diversity of sub‐daily precipitation characteristics in South America, the issues with conventional climate models and the uncertainty in satellite products and reanalyses. The growing interest in the DCP by the scientific community and the development of new techniques like convection permitting modelling will hopefully continue to improve our knowledge of the precipitation dynamics and its influence on climate. [ABSTRACT FROM AUTHOR]

This paper identifies two sources of uncertainties in model projections of temperature and precipitation: internal and inter-model variability. Eight models of WCRP-CMIP3 and WCRP-CMIP5 were compared to identify improvements in the reliability of projections from new generation models. While no significant differences are observed between both datasets, some improvements were found in the new generation models. For example, in summer CMIP5 inter-model variability of temperature was lower over northeastern Argentina, Paraguay and northern Brazil, in the last decades of the 21st century. Reliability of temperature projections from both sets of models is high, with signal to noise ratio greater than 1 over most of the study region. Although no major differences were observed in both precipitation datasets, CMIP5 inter-model variability was lower over northern and eastern Brazil in summer (especially at the end of the 21st century). Reliability of precipitation projections was low in both datasets. However, the signal to noise ratio in new generation models was close to 1, and even greater than 1 over eastern Argentina, Uruguay and southern Brazil in some seasons. [ABSTRACT FROM AUTHOR]