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Rainfall variability is a determining factor in climate risk and agricultural performance in tropical regions, as it conditions soil water availability. The objective of this study is to characterize annual and monthly precipitation in the municipality of Juína-MT, from 1985 to 2024, using descriptive statistics and anomalies, relating the observed patterns to ENSO (ONI) and discussing implications for agricultural planning according to ZARC. Daily precipitation data from HidroWeb/ANA were used, aggregated into monthly and annual totals. The mean, maximum, minimum, standard deviation, and coefficient of variation were calculated, as well as the monthly deviation (anomaly) in relation to the monthly climatology and its annual mean. Annual anomalies were compared with the Oceanic Niño Index and the ENSO phase, and the results were interpreted in light of the official ZARC windows. A seasonal regime compatible with Aw climate was found, with rainfall concentrated between November and March and a marked reduction in winter. High interannual variability and the occurrence of annual extremes were observed, as well as monthly maximums occurring at the peak of the rainy season, with a potential increase in erosion risks and operational restrictions. Standard 1 Undergraduate student in Agronomy, Universidade do Estado de Mato Grosso (UNEMAT), Aripuanã, Mato Grosso, Brazil. E-mail: claudineia.lima@unemat.br 2 Undergraduate student in Agronomy, Universidade do Estado de Mato Grosso (UNEMAT), Aripuanã, Mato Grosso, Brazil. E-mail: waldineiaferreira@hotmail.com 3 Undergraduate Student in Civil Engineering, Universidade do Estado de Mato Grosso (UNEMAT), Nova Mutum, Mato Grosso, Brazil. E-mail: tdmqueiroz@unemat.br deviation was higher during the rainy season, whereas the coefficient of variation was highest in the dry months, particularly July. The relationship between anomalies and ENSO proved to be non-deterministic. Juína has high seasonality and rainfall variability. It is recommended to integrate ZARC windows, conservation practices, and local climate monitoring to reduce production vulnerabilities.
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Oliveira et al. (2026) studied this question.
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