Given the increasing occurrence of landslides on the East Coast Northeast of Brazil (ECNEB), it is essential to understand its conditions and triggering factors because meteorological anomalies triggered by a landslide will threaten life and property in the region. In this sense, this research aimed to diagnose the meteorological conditions that triggered landslides in the ECNEB in May 2017, evaluate the terrain’s intrinsic conditions using elevation, slope, and susceptibility parameters and determine critical precipitation thresholds for the city with the highest number of landslide risk areas in the region. A dynamic downscaling experiment was carried out using the Regional Climate Model (RegCM) to verify the ability of this model to represent rainfall over the ECNEB. The results from the intrinsic factors showed that the ECNEB is highly susceptible to landslides with various high-risk sectors for landslides to the population. The extreme rainfall event was associated with the convergence of humidity at low levels over the ocean, which contributed to landslides in the ECNEB, mainly in the State of Pernambuco, where 67 landslides were registered. The RegCM numerical simulation underestimated the high daily rainfall signal seen on the Tropical Rainfall Measuring Mission satellite. It is suggested that sensitivity tests can be performed using other physical parameters to find the best model configuration for the ECNEB. This work recommends that exploring the relationship between precipitation and landslides will provide objective criteria for assessing risk areas by contributing to the predictability of disasters in this region.

Various studies have identified that between 2012 and 2017, Brazil’s semiarid region suffered severe drought. However, few studies have analyzed whether this drought also affected the eastern coastal region of Northeast Brazil (ENEB). Therefore, the objective of this work is to identify rainfall anomalies in these regions and verify the hydrometeorological impact on reservoirs in the 2012-2017 interval. For this purpose, we used precipitation data and atmospheric variables in the period from 1981 to 2017 to investigate the rainy season and associated dynamic patterns, as well as the consequences of these mechanisms on the variation of the water parameters of important reservoirs. The results indicated that rain events in the ENEB during 2012-2017 presented similar climatological behavior, without the characteristic of a drought event as observed in the semiarid region. The meteorological analyses showed that the combination of convergence with moisture over the Atlantic Ocean possibly favored greater frequency of shallow convective rainfall in ENEB, an important factor to explain the absence of generalized negative anomalies in the region. As a consequence, the reservoirs did not suffer from water collapse, unlike in the semiarid region.

In the year 2023, the Earth experienced the highest near‐surface temperature anomalies ever recorded until then. In addition, several extreme weather and climate events occurred around the world, including in Brazil. In this context, the primary objective of this study is to analyze the anomalous temperature and precipitation patterns observed in Brazil during 2023, along with the most significant extreme events. Different datasets and methodologies were applied. The north coast of the state of São Paulo had the highest accumulation of rainfall recorded in Brazil in a single day. September, October, and November 2023 experienced the large precipitation deficits over the Amazon region, leading to a very intense and prolonged drought. The south of Brazil was affected by a large amount of precipitation in a short time, associated with cyclones, resulting in fatalities and economic losses. Southeast and Central‐West Brazil experienced two intense heatwaves in the austral spring, breaking daily temperature records in major cities like São Paulo and Rio de Janeiro. Overall, this study describes the main physical processes responsible for these extremes, along with the socioenvironmental impacts caused by most of them.