Using a wavelet basis, we establish in this paper upper bounds of wavelet estimation on Lp(Rd) risk of regression functions with strong mixing data for 1≤p<∞. In contrast to the independent case, these upper bounds have different analytic formulae for p∈[1,2] and p∈(2,+∞). For p=2, it turns out that our result reduces to a theorem of Chaubey et al. (J Nonparametr Stat 25:53–71, 2013); and for d=1 and p=2, it becomes the corresponding