Temperature-economic growth relationships are computed to quantify the impact of climate change on the economy. However, model performance and differences of predictions among research complicate the use of climate econometric estimation. Machine learning methods provide an alternative that might improve the predictive effects. However, time series and extrapolation issues constrain methods such as random forests. We apply a simple thought experiment with national marginal GDP growth by aggregating subnational climate impact to alleviate the shortcomings in random forests. This paper uses random forests, multivariate cubic regression, and linear spline regression to examine the direct impacts of temperature on economic development and conducts a performance comparison of the methods. The model results indicate an optimal temperature of 15°C, 15°C or 21°C for each model. Furthermore, a thought experiment indicates that the marginal predictions of national GDP changes by approximately 1%, −3%, or −6% for models with 1°C warming. The performance comparison suggests that random forests have stable model performance and better prediction performance in bootstrapping. However, the extrapolation problem in random forests causes underestimation of climate impact in 5% of cells under 6°C warming. Overall, our results suggest that temperature should be considered in economic projections under climate change scenarios. We also suggest the use of more machine learning methods in climate impact assessment.