In this paper, we propose a novel prognostic approach for remaining useful life estimation with evolving joint prediction of continuous and discrete states which represent the signals and health states of systems respectively. The predictors are built with evolving capability of adapting structures and parameters online to capture the dynamic characteristics of systems during runtime. Moreover, the discrete states can be determined dynamically during the construction of the predictors for systems operating under different environments. In the testing phase, the optimum predictor for predicting continuous and discrete states jointly is chosen under the error and distance criteria. The RULs are estimated conveniently once the predicted signals fall into failure mode based on a distance metric. In order to validate the performance of the proposed approach, the widely used turbofan engine datasets are taken into consideration. Experimental results demonstrate the reasonability and superiority of the proposed approach compared to other approaches.