The spatiotemporal evolution and driving factors of global surface temperature from 1940 to 2022

In recent decades, the rising frequency and intensity of extreme heat events have made the investigation of spatiotemporal variations in global surface temperature (GST) and its driving mechanisms a critical research frontier. This study utilizes long-term global multi-source datasets to analyze GST trends from 1940 to 2022. By integrating correlation analysis with the Geographical Detector model, we establish a unified quantitative–spatial framework for identifying driving mechanisms. Within this framework, the impacts of atmospheric water vapor (WV), soil moisture (SM), total precipitation (TP), and the normalized difference vegetation index (NDVI), along with oceanic climate indices, are quantitatively assessed. The results show that GST increased by 0.99℃ during 1940–2022, with the fastest warming occurring in winter (0.023 ℃·yr −1 ). Pronounced warming is observed at high northern latitudes (>0.07 ℃·yr −1 ), with Europe experiencing the most rapid temperature rise (0.028 ℃·yr −1 ). Atmospheric water vapor is identified as the dominant driver of GST spatial patterns, amplified through synergistic interactions with NDVI and TP. Furthermore, El Niño and La Niña events strongly modulate GST anomalies, with regionally variable lag effects. Using a Long Short-Term Memory (LSTM) neural network, projections indicate a continued increase in GST, reaching 15.94 ℃ by 2032. These findings elucidate the complex, multi-factorial mechanisms governing global warming and its regional disparities, offering valuable insights for science-based climate mitigation and adaptation strategies.