This paper presents a joint learning algorithm for complex-valued time-frequency (T-F) masks in single-channel speech enhancement systems. Most speech enhancement algorithms operating in a single-channel microphone environment aim to enhance the magnitude component in a T-F domain, while the input noisy phase component is used directly without any processing. Consequently, the mismatch between the processed magnitude and the unprocessed phase degrades the sound quality. To address this issue, a learning method of targeting a T-F mask that is defined in a complex domain has recently been proposed. However, due to a wide dynamic range and an irregular spectrogram pattern of the complex-valued T-F mask, the learning process is difficult even with a large-scale deep learning network. Moreover, the learning process targeting the T-F mask itself does not directly minimize the distortion in spectra or time domains. In order to address these concerns, we focus on three issues: 1) an effective estimation of complex numbers with a wide dynamic range; 2) a learning method that is directly related to speech enhancement performance; and 3) a way to resolve the mismatch between the estimated magnitude and phase spectra. In this study, we propose objective functions that can solve each of these issues and train the network by minimizing them with a joint learning framework. The evaluation results demonstrate that the proposed learning algorithm achieves significant performance improvement in various objective measures and subjective preference listening test.