Changing global fire regimes including extended fire seasons due to climate change may increase the co-occurrence of high-impact fires that overwhelm national fire suppression capacities. These shifts increase the demand for international resource sharing to supplement national fire suppression efforts. In this paper, we explore the development and evaluate the effectiveness of international resource sharing arrangements of three regions: (1) The United States, Canada, and Australia (“Big Three”); (2) Europe; and (3) Southeast Asia by conducting a literature review of gray and peer- reviewed literature in combination with key informant interviews. For the “Big Three” and Europe, international resource sharing is perceived as necessary, effective, and continuously improving. Converging fire management processes and training and developing more effective administrative procedures facilitate these relationships. In Southeast Asia, political tensions and limited firefighting capacities have hampered effective cooperation. Formalized agreements of country-to-country support for fire management are nascent and evolving, and there is evidence that demand for expanding and improving these partnerships is increasing.

We apply a convergence research approach to the urgent need for proactive management of long-term risk associated with wildfire in the United States. In this work we define convergence research in accordance with the US National Science Foundation-as a means of addressing a specific and compelling societal problem for which solutions require deep integration across disciplines and engagement of stakeholders. Our research team brings expertise in climate science, fire science, landscape ecology, and decision science to address the risk from simultaneous and impactful fires that compete for management resources, and leverages climate projections for decision support. In order to make progress toward convergence our team bridges spatial and temporal scale divides arising from differences in disciplinary and practice-based norms. We partner with stakeholders representing US governmental, tribal, and local decision contexts to coproduce a robust information base for support of decision making about wildfire preparedness and proactive land/fire management. Our approach ensures that coproduced information will be directly integrated into existing tools for application in operations and policy making. Coproduced visualizations and decision support information provide projections of the change in expected number of fires that compete for resources, the number of fire danger days per year relative to prior norms, and changes in the length and overlap of fire season in multiple US regions. Continuing phases of this work have been initiated both by stakeholder communities and by our research team, a demonstration of impact and value.

Dynamical downscaling is a crucial process for providing regional climate information for broad uses, using coarser-resolution global models to drive higher-resolution regional climate simulations. The pool of global climate models (GCMs) providing the fields needed for dynamical downscaling has increased from the previous generations of the Coupled Model Intercomparison Project (CMIP). However, with limited computational resources, the need for prioritizing the GCMs for subsequent downscaling studies remains. GCM selection for dynamical downscaling should focus on evaluating processes relevant for providing boundary conditions to the regional models and be inspired by regional uses such as the response of extremes to changes in the boundary conditions. This leads to the need for metrics representing processes of relevance to diverse stakeholders and subregions of a domain. Procedures to account for metric redundancy and the statistical distinguishability of GCM rankings are required. Further, procedures for selecting realizations from ensembles of top-performing GCM simulations can be used to span the range of climate change signals in multiple ways. As a result, distinct weighting of metrics and prioritization of particular realizations may depend on user needs. We provide high-level guidelines for such region-specific evaluations and address how CMIP7 might enable dynamical downscaling of a representative sample of high-quality models across representative shared socioeconomic pathways (SSPs).