Climate change poses escalating risks to environmental, economic, and social systems worldwide, making accurate temperature forecasting a critical component of climate impact assessment and mitigation planning. Advances in data-driven modelling have expanded the range of tools available for analysing climate time series, complementing traditional statistical approaches. The continued increase in global surface temperatures, driven primarily by anthropogenic greenhouse gas (GHG) emissions, underscores the need for forecasting models capable of capturing complex and non-linear climate dynamics. This study compares the predictive performance of a Long Short-Term Memory (LSTM) neural network with a Seasonal Autoregressive Integrated Moving Average (SARIMA) model using historical global temperature data. The results show that LSTM outperforms SARIMA at the global scale, achieving an R2 of 0.9846, RMSE of 0.1528 °C, and MAE of 0.1198 °C, representing a 50.7% reduction in error relative to the SARIMA baseline (R2 = 0.9364; RMSE = 0.3100 °C). However, regional analyses reveal heterogeneous performance, with LSTM overestimating seasonal variability in certain regions, while SARIMA exhibits greater local stability. Sectoral emission analysis identifies agriculture and energy production as the dominant global contributors, with substantial regional variation. These findings suggest that hybrid modelling approaches may offer improved robustness for regional climate assessment and policy applications.
Ranasinghe et al. (Mon,) studied this question.