Abstract
Global warming has become a core challenge for human society. This study adopted the global surface temperature anomaly dataset from 1891 to 2023 released by the Japan Meteorological Agency (JMA). Multiple quantitative methods, including Sen’s slope estimation, Modified Mann–Kendall (MMK) test with pre-whitening, Pettitt test and GIS spatial analysis, were comprehensively applied to investigate the long-term climate change trends and regional response characteristics across the globe and China. The results indicated that the global warming rate reached 0.0802 °C per decade, while the warming rate of China was 0.1139 °C per decade, which is 42.0% higher than the global average level. Both global and Chinese temperature changes experienced three evolutionary stages, namely slow growth period, stagnation period and accelerated warming period, with an abrupt turning point occurring during 1979–1980, which was closely linked to the phase transition of Pacific Decadal Oscillation and atmospheric circulation adjustment. Obvious spatial differentiation characteristics of climate warming were identified in China, with a more rapid warming trend in northern and inland regions and a relatively slow warming rate in southern and coastal areas. Since 1980, regional accelerated warming has been driven by both anthropogenic activities and natural climate variability. The research findings can provide solid scientific support for formulating regional climate adaptation strategies and promoting collaborative global climate governance.
| Original language | English |
|---|---|
| Article number | 6126 |
| Journal | Sustainability (Switzerland) |
| Volume | 18 |
| Issue number | 12 |
| DOIs | |
| State | Published - Jun 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 13 Climate Action
Keywords
- JMA dataset
- driving mechanism
- global climate change
- regional response
- regional sustainable governance
- spatial differentiation
- sustainable adaptation
- temperature trend
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