This paper reveals that the regression-based trend-cycle decomposition of Hamilton (2018) introduces spurious cyclical dynamics when time series undergo structural breaks. Because the filter relies on an <i>h</i>-step ahead projection, trend adjustment mechanically lags behind regime shifts, inducing bias in the cyclical component. We propose a parsimonious modification using a dummy-variable structure that allows the regression intercept and autoregressive components to adjust to identified breaks. Extensive Monte Carlo simulations show that this approach substantially reduces Root Mean Squared Error (RMSE) across a wide class of data-generating processes. These gains are robust to variations in sample size, break timing, location and number, and signal-to-noise ratios, even when break dates are endogenously estimated. An empirical application to the Swiss franc/euro exchange rate demonstrates that our modification avoids the spurious cyclical spikes generated by standard filters during major policy shifts, ensuring a more reliable decomposition for macroeconomic inference.