diff --git a/draco/analysis/transform.py b/draco/analysis/transform.py
index 042834269..4401c880e 100644
--- a/draco/analysis/transform.py
+++ b/draco/analysis/transform.py
@@ -879,14 +879,20 @@ class ShiftRA(task.SingleTask):
     ----------
     delta : float
         The shift to *add* to the RA axis.
+    periodic : bool, optional
+        If True, wrap any time sample that is shifted to RA > 360 deg around to its
+        360-degree-periodic counterpart, and likewise for any sample that is shifted
+        to RA < 0 deg. This wrapping is applied to the RA index_map along with any
+        dataset with an `ra` axis. Default: False.
     """
 
     delta = config.Property(proptype=float)
+    periodic = config.Property(proptype=bool, default=False)
 
     def process(
         self, sscont: containers.SiderealContainer
     ) -> containers.SiderealContainer:
-        """Add a shift to the input sidereal cont.
+        """Add a shift to the input sidereal container.
 
         Parameters
         ----------
@@ -904,8 +910,39 @@ def process(
                 f"Expected a SiderealContainer, got {type(sscont)} instead."
             )
 
+        # Shift RA coordinates by delta
         sscont.ra[:] += self.delta
 
+        if self.periodic:
+            # If shift is positive, subtract 360 deg from any sample shifted to
+            # > 360 deg. Same idea if shift is negative, for samples shifted to < 0 deg
+            if self.delta > 0:
+                sscont.ra[sscont.ra[:] >= 360] -= 360
+            else:
+                sscont.ra[sscont.ra[:] < 0] += 360
+
+            # Get indices that sort shifted RA axis in ascending order, and apply sort
+            ascending_ra_idx = np.argsort(sscont.ra[:])
+            sscont.ra[:] = sscont.ra[ascending_ra_idx]
+
+            # Loop over datasets in container
+            for name, dset in sscont.datasets.items():
+                if "ra" in dset.attrs["axis"]:
+                    # If dataset has RA axis, identify which axis it is
+                    ra_axis_idx = np.where(dset.attrs["axis"] == "ra")[0][0]
+
+                    # Make sure dataset isn't distributed in RA. If it is, redistribute
+                    # along another (somewhat arbitrarily chosen) axis. (This should
+                    # usually not be necessary.)
+                    if dset.distributed and dset.distributed_axis == ra_axis_idx:
+                        redist_axis = max(ra_axis_idx - 1, 0)
+                        dset.redistribute(redist_axis)
+
+                    # Apply RA-sorting from earlier to the appropriate axis
+                    slc = [slice(None)] * len(dset.attrs["axis"])
+                    slc[ra_axis_idx] = ascending_ra_idx
+                    dset[:] = dset[:][tuple(slc)]
+
         return sscont