In developing countries, accurate rainfall estimation with adequate spatial distribution is limited due to sparse rain gauge networks. One way to solve this problem is the use of satellite-based precipitation products. These satellite products have significant spatial coverage of rainfall estimates and it is of fundamental importance to investigate their performance across space–time scales and the factors that affect their uncertainties. In the open literature, some studies have already analyzed the ability of satellite-based rain estimation products to estimate average rainfall values. These investigations have found very close agreement between the estimates and observed data. However, further evaluation of the satellite precipitation products is necessary to improve their reliability to estimate extreme values. In this scenario, the main goal of this work is to evaluate the ability of satellite-based precipitation products to capture the characteristics of extreme precipitation over the tropical region of South America. The products evaluated in this investigation were 3B42 RT v7.0, 3B42 RT v7.0 uncalibrated, CMORPH V1.0 RAW, CMORPH V1.0 CRT, GSMAP-NRT-no gauge v6.0, GSMAP-NRT- gauge v6.0, CHIRP V2.0, CHIRPS V2.0, PERSIANN CDR v1 r1, CoSch and TAPEER v1.5 from Frequent Rainfall Observations on GridS (FROGS) database. Some products considered in this investigation are adjusted with rain gauge values and others only with satellite information. In this study, these two sets of products were considered. In addition, gauge-based daily precipitation data, provided by Brazil’s National Institute for Space Research, were used as reference in the analyses. In order to compare gauge-based daily precipitation and satellite-based data for extreme values, statistical techniques were used to evaluate the performance the selected satellite products over the tropical region of South America. According to the results, the threshold for rain to be considered an extreme event in South America presented high variability, ranging from 20 to 150 mm/day, depending on the region and the percentile threshold chosen for analysis. In addition, the results showed that the ability of the satellite estimates to retrieve rainfall extremes depends on the geographical location and large-scale rainfall regimes.

Regional Climate Models (RCMs) provide climate information required for the evaluation of vulnerability, impacts, and adaptation at ner scales than their global driving models. As they explicitly resolve the basic conservation and state equations, they solve physics with more detail, conserving teleconnection of larger scales provided by GCMs. In South America (SA), the regional simulations have been historically evaluated principally on climatological aspects, but the representativeness of extremes still needs a deeper assessment. This study aims to analyze three RCMs driven by different GCMs: RegCM4-7, REMO2015, and Eta in the CORDEX SA region with focus on their capacity to reproduce extreme ETCCDI historical indices of daily precipitation and extreme temperature. Rx5day, CDD, TXx, and TNn were evaluated regarding the historical spatio-temporal variability and trends and climate change projections for the 2071-2099 period in the RCP8.5 were provided.The historical behavior of RCMs (1981RCMs ( -2005 was compared with two gridded products: CPC and Agrometeorological indicators derived from ERA5 reanalysis, previously compared with records from meteorological stations to assess potential observational biases. The results show that the highest observational uncertainty is observed in the regions with more scarce surface stations (North and West of SA) and with complex topography, being more pronounced in the precipitation-based indices. We found that RCMs generally show more agreement in the spatial variability than in the inter-annual variability for all the indices and SA regions. When analyzing the observed trends, all models better reproduce the long term variability of extreme temperature indices. More disagreement is present for Rx5day and CDD indices trends, including substantial spatial heterogeneities in both magnitude and sign of tendency. Climate change projections exhibited signi cant agreement to warmer conditions in TXx and TNn, but precipitation signals differed between RCMs and the driving GCM within each regional model. Maximum dry spells are expected to increase in almost all SA regions whereas the climate change signals in extreme precipitation events are more consistent over southeastern SA (northern and southwestern SA) with positive (negative) changes by the end of the century.

The main objective of this study is to assess the ability of several high-resolution satellite-based precipitation estimates to represent the Precipitation Diurnal Cycle (PDC) over Brazil during the 2014–2018 period, after the launch of the Global Precipitation Measurement satellite (GPM). The selected algorithms are the Global Satellite Mapping of Precipitation (GSMaP), The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data from different national and regional networks were used as the reference dataset after going through rigid quality control tests. All datasets were interpolated to a common 0.1° × 0.1° grid every 3 h for comparison. After a hierarchical cluster analysis, seven regions with different PDC characteristics (amplitude and phase) were selected for this study. The main results of this research could be summarized as follow: (i) Those regions where thermal heating produce deep convective clouds, the PDC is better represented by all algorithms (in term of amplitude and phase) than those regions driven by shallow convection or low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms the rest of the algorithms with lower bias and less dispersion. In this case, the gauge-adjusted version improves the satellite-only retrievals of the same algorithm suggesting that daily gauge-analysis is useful to reduce the bias in a sub-daily scale; (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) overestimates rainfall for almost all times and all the regions, while the satellite-only version provide better results than the final version; (iv) CMORPH has the better performance for a transitional regime between a coastal land-sea breeze and a continental amazonian regime. Further research should be performed to understand how shallow clouds processes and convective/stratiform classification is performed in each algorithm to improve the representativity of diurnal cycle.

The ability of the Regional Climate Model v4 (RegCM4) to simulate the surface radiation budget and hydrological balance variables over South America have been evaluated. For this purpose, a 34-year long simulation was carried out with the regional climate model RegCM4 over South America on the CORDEX domain. The model is forcing by ERA-Interim reanalysis. The results show that RegCM4 simulates the main patterns of the variables associated with the surface radiation budget and hydrological balance in the four seasons of the year compared to the observations (CLARA2 and CRU/PERSIANN). However, the cloudiness and surface radiation budget variables: Cloud Fraction Cover (CFC), net shortwave (SW) and longwave (LW) radiation at surface are overestimated, mainly over the oceans. This is associated with the errors in the CFC due to the deficiency of the model in representing the low-level clouds. Some differences are also noted in the hydrological balance. The intensity and temporal evolution of precipitation, especially in the central and southern Amazon, may be associated with the selected domain, which fails to adequately represent the influence of the adjoining oceans. In northern and northeast parts, the differences are associated with deficiencies of RegCM4 in representing precipitation rates. Although the deficiencies, taking into account that the model is capable to reproduce the general pattern of some important variables of the surface radiation budget and hydrological cycle, it may be a useful tool for climate studies.