<p>The climate change plays a key role in ecosystem evolution and has been proved to be affected by comprehensive factors including anthropogenic activities. The application of GCMs (General Circulation Models) launched by CMIP6 (Coupled Model Intercomparison Project Phase 6) has become a primary implement to catch future climate characteristics under different future socio-economic pathways. However, quantitative future climate change records with high credibility generated by robust GCMs merged dataset from CMIP6 is scare. The majority of former conclusions depend on traditional GCMs ensemble datasets (e.g., single, mean and medium) which have proved to be highly instable. In this study, 3 machine learning methods (Ordinary Least Squares regression, Decision Tree, and Deep Neural Networks) were applied to ensemble temperature and precipitation from 16 CMIP6 GCMs simultaneously. Monthly optimal estimation of precipitation and temperature from the three datasets were selected to generate a new ensemble dataset under three Socio-Economic Pathways (SSP1-2.6, SSP2-4.5 and SSP5-8.5). The new precipitation (temperature) ensemble dataset with the R=0.81 (0.99) is more accurate than all the single GCM. High credible analyses demonstrate that Europe and North America contribute more to global warming than Oceania, Africa and South America. The global continent break through 1.5 &deg;C, 2 &deg;C and 3 &deg;C rising threshold in 2024, 2031 and 2048 under SSP5-8.5 scenarios, of which the driving capacity for global warming ranks first. Most precipitation aggregates in July and August, while dry months fall in April and September to next February till the end of 21^st century. Global precipitation will be accelerated polarization with the decreasing trends of Africa and Asia (<em>p</em> &lt; 0.05) under the scenario of SSP5-8.5. The proposed analysis provides credible opportunities and quantitative fundamental to understand future climate characteristics for ecology and meteorology.</p>