Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

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.

Tropical rainforests provide essential ecosystem services to agricultural areas, including moisture recycling. In the Amazon basin, drought frequency has increased in the late 20th and early 21st centuries, but the role of forests, ocean, and nonforested areas in causing or mitigating drought has not been determined. Using a precipitationshed moisture tracking framework, we quantify the contribution sources of evaporation to rainfall in Rondônia in the Brazilian Amazon. Forests account for ∼48% of annual rainfall on average, and more than half of the forest source is from protected areas (PAs). During droughts in 2005 and 2010, moisture supply decreased from oceans and nonforested areas, while supply from forests was stable and compensated for the decrease. Remote sensing and land surface models corroborate the relative insensitivity of forest evapotranspiration to droughts. Forests mitigate drought in the agricultural study region, providing an important ecosystem service that could be disrupted with further deforestation.

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.