Wireless channel characteristics are random in nature. Consideration of shadowing and fading effect in the channel is necessary so as to capture the behavior of signal variation at the receiving end. The composite effect of the phenomenon is prominent in applications where receiving nodes are deployed in deep faded region with shadowed signal propagation. This becomes more important as the average power of the signal becomes random due to fading and shadowing effects. This effect is more prominent to capture the random behavior of the channel one has to adopt the probabilistic framework by using probability distributions. The Rayleigh–lognormal distribution (RLD), useful in characterizing fading and shadowing aspects of wireless communication channel, has a complex integral having no closed form. Noting that inverse Gaussian distribution closely approximates lognormal distribution, a better approximation is proposed by generalized inverse Gaussian distribution (GIGD). While different combination of compound probability distribution is capable of capturing the behavior of the channel, no such compound distributions work for the complete range of channel variations. Thus, the probability density function of the resulting Rayleigh–generalized inverse Gaussian distribution (RGIGD), besides having a closed form and being analytically tractable, is a more appropriate substitute for RLD. Based on Kullback–Leibler (K–L) measure to assess closeness of two probability distributions, it is found that the proposed RGIGD distribution is closer to the original RLD than other distributions, viz. RGD, RIGD. Further, the proposed distribution can be considered to provide a solution for a large range variation in the sense that RGD and RIGD become its special cases and yields more realistic values of quality of service parameters along with flexibility of covering a wide range of the signal variations through its special cases. © 2021, Shiraz University.