In this study, we combined the CRU 4.0 and GLDAS Noah 2.1 datasets and used the random forest algorithm (RF) to calculate the monthly actual evapotranspiration (ETa) in southwestern China between 1966 and 2016. The accuracy of our model and the inversion results were evaluated by the MSE_OOB, PVE, and RMSE indices and a comparison with other typical datasets. Based on this, the spatiotemporal distribution and evolution pattern of ETa over multiple time scales were fully discussed. We extended our research by using the PIM model to evaluate the importance of the feature factors in each pixel. We obtained the following results:1) the mean value and the standard deviation (Std Dev) of MSE_OOB were 4.14 and 3.73, and those of PVE were 99.36% and 0.33; in addition, the mean RMSE of the monthly inversion results for 2000 to 2016 was 1.04 mm per month and the corresponding Std Dev was 0.52; moreover, the R² values for the inversion results of ETa from GLDAS 2.1, GLDAS 2, and MOD16 were 0.99, 0.89, and 0.95, respectively. All these evaluation indices illustrated the credibility and precision of the model and the inversion results were sufficiently high. 2) Our inversion results indicated that ETa increased with a decrease in latitude and gradually increased from the northwest plateau to the southeast coastal area; in addition, the spatial distribution patterns in southwestern China in different seasons were quite different:from spring to summer, high ETa expanded from the southeast to the northwest; in contrast, in winter, ETa diminished remarkably from northwest to southeast. The maximum value was reached around July every year and had the lowest value was reached around February, which showed periodic characteristics with fluctuations. 3) We found that the Hengduan Mountains were the boundary of the driving factors for ETa in arid and humid regions. The ETa in the humid regions south of the mountains was jointly driven by the cloud cover percentage (CCP), diurnal temperature range (DTR), and monthly average daily maximum temperature (TMX). In contrast, ETa in arid regions north of the mountains was mainly affected by CCP, frost day frequency (FDF), and vapor pressure (VAP). Notability, CCP is the most important driving factor weather in both humid and arid areas.