Abstract: The melting of Antarctic glaciers, the increasing of hurricanes and the gradual rising of sea level make people aware of the great challenges caused by global warming. So it is necessary to do research on global climate change. Missing data imputation is taken to study the data of four representative provinces in Canada, and a vector autoregressive (VAR) model is established considering the factors of solar radiation intensity, carbon dioxide content, soil water content, temperature, rainfall etc. to study Canada’s climate change. The specific model is established by doing stability test, impulse response and variance analysis, and is used to predict the temperature and precipitation in Canada. The experimental results show that the average temperature in Canada in the next 25 years will reach 15.0410 ℃, and the average precipitation will reach 2.0950 mm.

Key words: global warming, vector autoregressive model, temperature prediction, stationary test, variance analysis