Internal migration between Chinese provinces has increased substantially since the mid-1980s. Though it is generally agreed that this has been driven by economic factors, climatic factors might also have had a part to play. The challenge is to evaluate the impact of climatic factors on migration in the simultaneous presence of changing socio-economic influences. We resolve this challenge by carrying out a statistical multivariate regression analysis on bilateral migration rates between Chinese provinces. The analysis simultaneously includes climate change in the form of climate anomalies (temperature, precipitation, sunshine) and various socio-economic factors including energy consumption. To this end we have constructed a unique three-dimensional panel dataset (time, sending province, receiving province) with bilateral migration rates between 30 provinces for the period 1987-2015. Due to the distributional properties of the data and underlying theory we use a Poisson Pseudo Maximum Likelihood (PPML) estimator but include OLS estimates for comparison. The results suggest that increases in temperature and precipitation are significant migration push factors while increased sunshine discourages push migration. Provincial differentials in per capita energy consumption and Gross Regional Product (GRP) are also significant drivers of migration.