Burned area, fire severity, and suppression expenditures have increased in British Columbia in recent decades with climate change. Approximately 80% of suppression expenditures are attributable to wildfires near the Wildland–Urban Interface (WUI). Evaluating the potential for fuel management to reduce suppression expenditures is essential to mitigating demands on fire response resources and reducing impacts on communities. One management approach is to increase the proportion of deciduous tree species, which have a lower propensity for crown fire. Using fire suppression expenditure data from 1981 to 2014, we applied the machine learning method causal forests (CFs) to estimate the effect of the proportion of conifer forest cover on suppression expenditures for WUI fires and how these effects varied with other influential factors (i.e., heterogenous treatment effects). Across all fires, the effect of conifer cover on suppression expenditures was stronger on private land compared to public land, under high fire danger measured by daily severity ratings (DSRs), which reflect wind speed and fuel moisture, and for fires igniting earlier in the calendar year, based on Julian day. These findings provide insights into prioritizing wildland fuel treatment when budgets are limited. The CFs approach demonstrates potential for broader applications in fire risk mitigation and analysis beyond the scope of the current data. CFs may also be valuable in other areas of forest research where heterogenous treatment effects are common.

High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea surface wind. In particular, studies have shown that flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different 10-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved-scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modeled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea surface flux enhancements dependent on wind variability.