While the recently-developed first-order Takagi-Sugeno-Kang (TSK) fuzzy regressor FIMG-TSK shares its full interpretability, this study leverages the concisely expressed output variance of FIMG-TSK to explore its high feasibility in being a wide ensemble component. In this way, the regression performance can be enhanced and simultaneously FIMG-TSK's over-dependence on the rule weights can be alleviated to a certain extent. To this end, a wide ensemble of all base regressors (i.e., FIMG-TSKs) called EFIMG-TSKs is proposed. In the ensemble-strategic aspect, EFIMG-TSKs has its internally & generatively decorrelated ensemble strategy with a quintuply diversity guarantee for its strong generalization capability. In the learning aspect, the learning objective of EFIMG-TSKs reflects the internally & generatively decorrelated ensemble learning of all base FIMG-TSKs and accordingly is optimized globally with an analytical solution to the weights of all fuzzy rules in each base FIMG-TSK. Experimental results on sixteen benchmarking datasets demonstrate the effectiveness of EFIMG-TSKs in terms of regression performance, training time and interpretability.