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Tourism is an important industry to many regions around the world and has the potential to substantially impact local communities. Climate change is expected to influence tourism since weather patterns help determine where and when people travel. In this analysis, the effect of weather conditions on tourism-related spending at three geographically distinct locations in Maine, United States, was evaluated. A nonparametric method (boosted regression trees) was used to first identify the relative influence of twenty-two weather variables as predictors of tourism spending. Following this, a parametric model was constructed to statistically evaluate tourism spending across different measures and predict potential spending changes due to a warming climate. Results indicated that warmer temperatures increased tourism spending in the summer and fall, but had more varying results in the winter. Findings suggest tourism businesses in Maine and other relatively colder destinations could capitalize on potential gains in warmer months. [ABSTRACT FROM AUTHOR]