Residual seasonality (RS), i.e., the presence of seasonal patterns in seasonally adjusted data, can be present in hierarchical time series that are indirectly adjusted; however, it is important that – while aggregation relations are preserved – no RS is manifested. In the case of U.S. Gross Domestic Product (GDP) and its sub-aggregates, it is important that there be no RS at monthly or quarterly frequencies, while preserving aggregation relations between the various sub-components. One of the key challenges is that both temporal (from monthly to quarterly frequency) and hierarchical (from disaggregate variables to topline numbers) aggregates of RSfree time series can themselves exhibit RS – a phenomenon that has long been observed in the literature on indirect seasonal adjustment. The paper first provides a topological analysis of the self-intersecting lattice structure of accounting relations, decomposing the lattice into ordered subsets (called terraces) that can be sequentially analyzed. Secondly, the paper proposes a reconciliation method that minimally modifies each monthly time series such that its higher aggregates – as well as its quarterly aggregates – have no RS. This is accomplished by minimizing a relative difference criterion subject to nonlinear constraints furnished by seasonal adjustment diagnostic statistics, proceeding from terrace to terrace of the lattice in a top-down procedure. The method is applied to the Personal Consumptions Expenditures sub-lattice of GDP.