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This study examines the lived experiences and adaptation strategies of small-scale farmers in the southwestern Brazilian Amazonian state of Rondônia, amidst escalating climate challenges. Through nine in-depth interviews, it uncovers the impact of unpredictable weather, increased temperatures, and shifting precipitation on agriculture and livelihoods. Participants, ranging from family farmers to agricultural collective members, detail shifts from traditional crop cultivation to more resilient practices like cattle ranching and dairy production. The narratives reveal a deep understanding of local climate volatility and its direct effects on water availability, crop viability, and livestock productivity. Farmers describe adaptation measures including new crop varieties, irrigation systems, and strategic land use to enhance biodiversity and mitigate climate change effects. Despite these adaptations, challenges like water scarcity, high input costs, and the need for technical assistance remain prominent. Farmers emphasize the need for stronger support systems, highlighting community solidarity, governmental aid, and access to sustainable technologies and education as essential for climate adaptation. They call for policies providing equitable resources and support, underscoring the importance of inclusive climate governance that acknowledges the unique vulnerabilities and contributions of Rondônia’s agricultural sector. This research contributes to understanding how climate change reshapes rural Amazonian communities, arguing that ongoing deforestation and climatic changes threaten regional agricultural stability. It advocates for targeted policy interventions to provide technical assistance for sustainable farming and climate adaptation, alongside mechanisms to support fair market pricing. These measures are essential for enhancing the resilience and sustainability of local farming communities amidst climate change. 

The Brazilian Amazon provides important hydrological cycle functions, including precipitation regimes that bring water to the people and environment and are critical to moisture recycling and transport, and represents an important variable for climate models to simulate accurately. This paper evaluates the performance of 13 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. This is done by discussing results from spatial pattern mapping, Taylor diagram analysis and Taylor skill score, annual climatology comparison, cumulative distribution analysis, and empirical orthogonal function (EOF) analysis. Precipitation analysis shows: (1) This region displays higher rainfall in the north-northwest and drier conditions in the south. Models tend to underestimate northern values or overestimate the central to northwest averages. (2) The southern Amazon has a more defined dry season (June, July, and August) and wet season (December, January, and February) and models simulate this well. The northern Amazon dry season tends to occur in August, September, and October and the wet season occurs in March, April, and May, and models are not able to capture the climatology as well. Models tend to produce too much rainfall at the start of the wet season and tend to either over- or under-estimate the dry season, although ensemble means typically display the overall pattern more precisely. (3) Models struggle to capture extreme values of precipitation except when precipitation values are close to 0. (4) EOF analysis shows that models capture the dominant mode of variability, which was the annual cycle or South American Monsoon System. (5) When all evaluation metrics are considered, the models that perform best are CESM2, MIROC6, MRIESM20, SAM0UNICON, and the ensemble mean. This paper supports research in determining the most up-to-date CMIP6 model performance of precipitation regime for 1981–2014 for the Brazilian Amazon. Results will aid in understanding future projections of precipitation for the selected subset of global climate models and allow scientists to construct reliable model ensembles, as precipitation plays a role in many sectors of the economy, including the ecosystem, agriculture, energy, and water security. 

Abstract Rainforest in protected areas in the Brazilian Amazon is at risk due to increasing economic pressures and recent weakening of environmental agencies and legislation by the federal administration. This study examines the impacts of deforestation in protected areas on dry‐season precipitation in the Brazilian state of Rondônia located in the southwestern Brazilian Amazon. Regional‐climate model simulations indicate that clearing protected forests in Rondônia would result in substantial changes to the surface energy balance, including increased sensible and decreased latent heat flux. Consequent changes to low‐level wind circulation would enhance moisture flux convergence and convection over the newly deforested areas, leading to enhanced rainfall in those areas. However, deforestation of protected areas would decrease dry season rainfall up to 30% in the existing agricultural region, with potentially important negative impacts on agricultural production. Additionally, our results indicate that following deforestation, the newly degraded areas will experience warmer and drier afternoons that could place the remaining natural vegetation under vapor deficit stress.