A recursive identification method is proposed to obtain continuous-time state-space models in systems with nonuniformly sampled (NUS) data. Due to the nonuniform sampling feature, the time interval from one recursion step to the next varies and the parameter is always updated partially at each step. Furthermore, this identification method is applied to form a combined data compression method in NUS processes. The data to be compressed are first classified with respect to a series of potentially existing (possibly time-varying) models, and then modeled by the NUS identification method. The model parameters are stored instead of the identification output data, which makes the first compression. Subsequently, as the second step, the conventional swinging door trending method is carried out on the data from the first step. Numeric results from simulation as well as practical data are given, showing the effectiveness of the proposed identification method and fold increase of compression ratio achieved by the combined data compression method. A recursive identification method is proposed to obtain continuous-time state-space models in systems with nonuniformly sampled (NUS) data. Due to the nonuniform sampling feature, the time interval from one recursion step to the next varies and the parameter is always updated partially The data to be compressed are first classified with respect to a series of potentially existing (possibly time-varying) models, and then modeled by the NUS identification method. The model parameters are stored instead of the identification output data, which makes the first compression. which makes the first compression. simulation as well as practical data are given, showing the effectiveness of the proposed identification method and fold increase of compression rat io achieved by the combined data compression method.