Lifted ( family-based ) static analysis based on abstract interpretation is capable of analyzing all variants of a program family (or any other configurable software system), simultaneously, in a single run without generating any of the variants explicitly. The elements of the underlying lifted domain are tuples, which maintain one property per system variant. Still, explicit property enumeration in tuples, one by one for all variants, immediately yields combinatorial explosion. This is particularly apparent in the case of program families that, apart from Boolean features, contain also numerical features with large domains, thus giving rise to astronomical configuration spaces. The key for an efficient lifted analysis is a proper handling of variability-specific constructs of the language (e.g., feature-based runtime tests and #if directives). In this work, we introduce new symbolic representations of the lifted domain that can efficiently analyze program families with numerical features. This makes sharing between property elements corresponding to different variants explicitly possible. In the first approach, elements of the new lifted domain are decision trees , in which decision nodes are labeled with linear constraints defined over numerical features and the leaf nodes belong to an existing single-program analysis domain. The lifted domain is parametric in the choice of the domains for representing linear constraints and leaf nodes. Furthermore, we propose another alternative approach for efficient lifted analysis. We encode a program family with numerical features as a family with only Boolean features, and then use a BDD lifted domain to analyze the resulting program family. To illustrate the potential of our representations, we have implemented an experimental lifted static analyzer, called SPLNum 2 Analyzer , for inferring invariants of #if -annotated C programs. The tool implements all three approaches for lifted analysis based on abstract interpretation: tuple-based, decision tree-based, and BDD-based. It uses existing numerical abstract domains (e.g., intervals, octagons, polyhedra) from the APRON library as parameters. An empirical evaluation on benchmarks from SV-COMP and BusyBox yields promising results indicating that our tool can be successfully used for analyzing program families with very large configuration spaces.