Lombard speech is produced in noisy environments due to the Lombard effect and is intelligible in adverse environments. To adaptively control the intelligibility of transmitted speech for public announcement systems, in this study, we focus on perceptually mimicking Lombard speech under backgrounds with varying noise levels. Other approaches map corresponding neutral speech features to Lombard speech features, but as this can only be applied to one noise level at a time, it is unsuitable for varying noise levels because the characteristics of Lombard speech are varied according to noise level. Instead, we utilize a rule-based method that automatically generates rules and flexibly controls features with any change of noise level. Specifically, we conduct a feature tendency analysis and propose a continuous rule generation model to estimate the effect of varying noise levels on features. The proposed techniques, which are based on a coarticulation model, MRTD, and spectral-GMM, can easily modify neutral speech features by following the generated rules. Voices having these features are then synthesized by STRAIGHT to obtain Lombard speech fitting to noises with varying levels. To validate our proposed method, the quality of mimicking speech is evaluated in subjective listening experiments on similarity, intelligibility, and naturalness. In varying noise levels, the results show equal similarity with Lombard speech between the proposed method and a state-of-the-art method. Intelligibility and naturalness are comparable with some feature modifications.