RandomForestRegressor for classification problems
The Random Forest regressor does differ somewhat from the Random Forest classifier when it comes to ensembling the decision trees:

The classifier uses the mode of the predicted classes of the decision trees
The regressor uses the mean of the predicted values of the decision trees

Due to this difference the models can have different results. And in some cases this might result in the regressor performing better than the classifier. 
In addition to that I would say that if you tune your hyperparameters correctly, the classifier should perform better on a classification problem than the regressor.
I've been doing Applied Machine Learing in Python course on coursera and on Assignment of week 4 I`ve found something interesting. During my first attempt to complete the assignment I tried using RandomForestClassifier from sklearn to predict labels, but the model was overfitting and was showing poor test accuracy results. As an experiment I switched to RandomForestRegressor and, guess what, not only did it not overfit, but test accurary was also a lot higher. So, why does RandomForestRegressor perform a lot better on a binary classification problem?