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The states of Mato Grosso (MT) and Mato Grosso do Sul (MS) are located in Midwest Brazil and are dependent on agribusiness, which makes their water regimes of fundamental importance. However, the existing weather stations in these states are limited, and rainfall products are therefore a valuable alternative. The objectives of this study are: (a) to validate the CHIRPS datasets for the states of MT and MS in the Brazilian Midwest region; (b) to evaluate the intraseasonal variability of regional rainfall via CHIRPS; and (c) to define the drought and wet periods on the decennial scale, based on the annual SPI. Three seasons were defined (rainy, drought, and transition), and the differences showed that the MS rainy season (56.65%) was reduced due to the veranicos that occur in February, with an increase in the percentages of the drought (13.83%) and transition (29.52%) seasons. The highest monthly rainfall occurs in the north of MS (328–390 mm), while intermediate rainfall (109–273 mm) occurs in the south of MS. This satisfactorily represents the formation of a rainfall gradient in a N–S direction during the rainy season, similar to other characterizations around the world. Based on the annual SPI assessed per decade, there were moderate droughts annually in the 1990s, 2000s, and 2010s, but not in the 1980s. There were significant differences between the decades due to the intensity and categorization of the ENSO phases (strong and moderate), and drier decades with moderate rainfall reduction in both states can be defined in the last 40 years. The data obtained via CHIRPS are satisfactory for the spatio‐temporal evaluation of regional rainfall, and for the use of SPI to detect drought and wet periods in the states of MT and MS. [ABSTRACT FROM AUTHOR]

Fire is used in the management of pastures, renewal and expansion of areas, and agricultural activities in South America (SA). The objectives of this study were: i) to identify the countries and regions with the highest number of fire foci in SA, and ii) to evaluate the spatial dynamics of fire foci based on the Meteorological Fire Danger Index (MFDI) and future scenarios through numerical simulations. Fire foci time series comprised 21 years (1998–2018) from the BDQueimadas database. Cluster Analysis (CA), descriptive and exploratory statistics were employed. Fire foci maps for SA were made in 10-km pixel dimensions. MFDI was used to assess fire danger via SPEEDY (Simplified Parametrizations, primitivE-Equation DYnamics) model simulations. Three simulations were performed: control scenario (1980–2015), RCP2.6 scenario (optimistic - 2016 and 2050), and RCP8.5 scenario (pessimistic - 2015 and 2050). Regionally, three homogeneous groups of fire foci (G1, G2 and G3) and one atypical (NA - Not Grouped) were identified for Brazil via CA. The highest fire foci occurred in Brazil (62.72%), followed by Bolivia (9.03%), Argentina (8.28%), Venezuela (6.11%), Paraguay (5.94%), and Colombia (3.87%), respectively. The highest density of fire foci occurred in the MATOPIBA region, the confluence of Maranhão, Tocantins, Piauí, and Bahia, - (agricultural frontier), and also in the Cerrado-Amazon transition and the states of Mato Grosso and Mato Grosso do Sul in Brazil, followed by Paraguay, Bolivia, Venezuela, and Argentina. The countries and regions of Brazil do not change, only intensify from year to year, and such fire foci variability may be associated with the El Niño-Southern Oscillation (ENSO) phases. The control scenario identified in east-central Brazil, western Bolivia, Paraguay, and northern Argentina. The optimistic scenario showed an improvement in some countries and a worsening in the territorial distribution in Brazil, Venezuela, and Colombia. The pessimistic scenario identified increased degradation compared to the previous scenarios in almost all SA countries. • Brazil, Bolivia, Argentina, Venezuela, and Paraguay largest records of fire foci in South America. • Projections of future fire risk scenarios indicate central-eastern Brazil, Bolivia, Paraguay, and Argentina. • The integrated analysis identified land use and climatic variability as the main factors. [ABSTRACT FROM AUTHOR]