This study provides the first estimates of the frequency distribution of observational and future maximum daily precipitation considering nonstationarity for the entire Brazilian territory. We assess observational data from 1980 to 2015 and projected data from 2020 to 2099 for two climate change scenarios and four downscaled climate models. We modelled extreme precipitation according to the extreme values theory and calculated the precipitation intensity associated with the return periods of 5, 10, 25, 50, and 100 years in nonstationary conditions. The results indicate that nonstationarity is identified in 17.5% of the study area during 1980–2015. The analysis of future climate projections indicated an increase in the return levels of extreme precipitation compared to the historical period in at least 90% of the national territory. It shows that engineering design must urgently consider the nonstationarity of extreme precipitation under the risk of increasingly unsafe infrastructure.

Extreme weather events can considerably impact agriculture because crop production depends on favorable climatic conditions. Additionally, extreme climatic events are expected to increase in frequency and intensity. However, crop diversification helps to reduce vulnerability to climate risks. In this context, the present research analyzes the process of agricultural diversification, understanding its determinants and role as an adaptive measure in Brazil. Thus, fixed-effects panel models allow us to estimate the effects of extreme weather indices on crop diversification in 3818 Brazilian municipalities. We find that Consecutive Dry Days (CDD), Dry Days (Rn1mm), and Hot days (TX90p) positively affect diversification, while for Frosts (FD) and Extreme Precipitation Days (R20mm), there are non-statistically significant effects. So, the response to diversification as an adaptive strategy would depend on the type of climatic shock. Results show that access to technical assistance, farm size, and corn production significantly influenced diversification. Then, we analyze the effect of climate change on diversification according to future scenarios. Based on simulations, Brazilian municipalities would diversify their crops as the climate scenario is expected to get more severe. The simulations predict that the frequency of hot days will increase between 2045 and 2065. Therefore, crop diversification is expected to increase in both the RCP4.5 and RCP8.5 scenarios. These results can help promote the use of resilient technologies by considering the types of extreme weather events and regional differences and tailoring policies to meet the specific challenges farmers face in each region.

In developing countries, such as Brazil, farmers are susceptible to extreme events.The Brazilian federal government created insurance programs to mitigate extreme event effects, such as frost, called “Programa de Subvenção ao Prêmio do Seguro Rural”. Frost is an atmospheric phenomenon that causes damage to plants due to low temperatures that exceed their resistance to freezing. This paper seeks to verify the risk level of frost in Brazil, connecting this information with farmers, who contracted Rural Insurance to protect themselves from this extreme event. To reduce possible biases in calculating the frost probability, this study uses two climatological databases. The majority of the Brazilian territory has very low probabilities of this phenomenon occurring. In the databases used to verify the probability of a temperature below 2 °C, the data proposed by Sheffield paper, show that, in part of the Brazilian territory, there is less chance of frost than Xavier's data. Regardless of the database used, in Brazil, there are farmers who contract frost insurance in areas where the risks are close to zero.

We analyzed the spatial patterns (1985-2018) of planted area and productivity of beans, cassava and corn, the main crops grown under rainfed conditions intended for food production in family farming in the Brazilian semiarid region. The spatial construction used data collected from Municipal Agricultural Surveys combined with remote sensing data. In general, the maps indicate heterogeneous patterns, with a marked decrease in the planted area, in the spatio-temporal evolution of agricultural variables. Cassava is the one with the lowest occupation of planted areas (~11%), followed by beans (~43%) and corn (~46%). On the other hand, beans are the crop that lost the most growing space in the study region (37 thousand ha), followed by cassava (14 thousand ha) and corn (12 thousand ha) per year. In relation to the productivity of the crops analyzed, the corn crop stands out, which, even with the reduction of the planted area, increased productivity by more than 160%. Bean and cassava crops reduced their yields by 10% and 34%. The results generated indicate a drop, both in the planted area and in productivity for the three crops analyzed (except corn productivity), which raises awareness of local food security and advances in climate change.