Adequacy of logit model with oversampled data

I fitted a binary logit model with unbalanced data which were oversampled using SMOTE. This gave an excellent ROC curve but very poor adequacy - the zero hypothesis of adequacy was rejected by Hosmer-Lemeshow test and le Cessie – van Houwelingen – Copas – Hosmer unweighted sum of squares test. However, the logit model for the original data (without oversampling) had very good adequacy statistics (but mediocre classification properties).
Are there any ways to oversample data without ruining adequacy statistics for a binary logit/probit model? Thanks in advance.

Best wishes, Ievgen.