The objective of this study was to evaluate the regimes of temperature and rainfall in Belém, PA, Brazil, with emphasis on the start of the dry season in order to provide planning support for agricultural activities during years of climatic anomolies in the region. An initial analysis was done of the metropolitan region of Belém comparing it to the typology of Amazonian climates using rainfall data from 1971 to 2014 and creating an annual index of precipitation anomolies (AIPA). The temperature regime was described using a homogeneous rainfall dataset from 1990 to 2014. The hydrological balance was estimated for the period 1990 to 2014 using an index of capacity of soil water availability equal to 100 mm to identify the months with deficit or excess of soil water. Box plots were analyzed by decade and maximums of daily rainfall for the month of August were used. The Pareto principle was applied to 9 indices to assess the effects of rainfall quantity in relation to anomalous years. Although the metropolitan region of Belém is for the most part categorized by the Af2 climate type it is possible to have prolonged soil water deficit from August through November, an effect that is intensified by the El Niño phenomenon. Furthermore, in the month of August there were years with extreme rainfall events, such as that of August 7th, 2010 where 72.4 mm of rainfall occurred representing 53% of total monthly rainfall. This event can be explained by the intense waves of humidity coming from the East that amplified local rainfall. During the last two decades extreme daily rainfall events have become more frequent, and rainfall reductions in the region have tended to intensify in areas that historically receive less rainfall such as the transition between the Amazonian and Savannah biomes. Therefore, in La Niña or El Niño years, the month of August can be considered to be the signal for meso- and large-scale atmospheric mechanisms that influence precipitation regimes and that can have a negative effect of the region’s agricultural productivity. Key words: Precipitation rate, El Niño, La Niña.