The case-crossover study design is used to study the triggers of acute outcomes in populations. It controls for all measured and unmeasured time-invariant confounders by design. Studies of environmental triggers of morbidity are potentially confounded by temporal trends in the outcome owing to omitted covariates. We conducted a simulation study of the case-crossover design's ability to control for temporal confounding patterns by design rather than through modeling. We compared five case-crossover control sampling strategies including the matched pair, a symmetric bi-directional, a total history approach, and two approaches proposed by Navidi (Biometrics 1998;54:596-605). We simulated true relative risks (RR) of 1.10 and 2.00 and induced confounding by seasonal patterns as well as linear and nonlinear long-term trends to yield estimated RR values as high as 3.18. The symmetric bi-directional approach was compared across four lag times and controlled for temporal confounding best when the lag was shortest. With a 1-week lag, it estimated the RR values as 1.10 and 2.01. The four other approaches failed to control for the temporal trends. Our simulations show that the symmetric bi-directional case-crossover design can substantially control for temporal confounding by design although it is not as efficient (66%) as Poisson regression analysis.