diff --git a/mne/viz/tests/test_topomap.py b/mne/viz/tests/test_topomap.py
index 64a13b46006..4fce5aeffbf 100644
--- a/mne/viz/tests/test_topomap.py
+++ b/mne/viz/tests/test_topomap.py
@@ -472,10 +472,7 @@ def get_texts(p):
     fig1 = evoked.plot_topomap(
         "interactive", ch_type="mag", proj="interactive", **fast_test
     )
-    # TODO: Clicking the slider creates a *new* image rather than updating
-    # the data directly. This makes it so that the projection is not applied
-    # to the correct matplotlib Image object.
-    # _fake_click(fig1, fig1.axes[1], (0.5, 0.5))  # click slider
+    _fake_click(fig1, fig1.axes[1], (0.5, 0.5))  # click slider
     data_max = np.max(fig1.axes[0].images[0]._A)
     proj_fig = plt.figure(plt.get_fignums()[-1])
     assert fig1.mne.proj_checkboxes.get_status() == [False, False, False]
diff --git a/mne/viz/topomap.py b/mne/viz/topomap.py
index 1bd585bb4f1..b1fb4623f77 100644
--- a/mne/viz/topomap.py
+++ b/mne/viz/topomap.py
@@ -323,6 +323,10 @@ def _plot_update_evoked_topomap(params, bools):
     new_evoked.apply_proj()
 
     data = new_evoked.data[:, params["time_idx"]] * params["scale"]
+    _fig = params.get("fig")
+    _mne_params = getattr(_fig, "_mne_params", None) if _fig is not None else None
+    if _mne_params is not None and "current_time_idx" in _mne_params:
+        data = new_evoked.data[:, [_mne_params["current_time_idx"]]] * params["scale"]
     if params["merge_channels"]:
         data, _ = _merge_ch_data(data, "grad", [])
 
@@ -2375,6 +2379,8 @@ def plot_evoked_topomap(
         # if ch_type in _fnirs_types:
         if modality != "other":
             merge_channels = False
+    merge_ch_type = ch_type
+    merge_names = names
     # apply mask if requested
     if mask is not None:
         mask = mask.astype(bool, copy=False)
@@ -2460,6 +2466,12 @@ def plot_evoked_topomap(
         )
         slider.vline.remove()  # remove initial point indicator
         func = _merge_ch_data if merge_channels else lambda x: x
+        # Store merge metadata on the figure so slider callbacks can access it
+        fig._mne_params = dict(
+            merge_ch_type=merge_ch_type,
+            merge_names=merge_names,
+            merge_channels=merge_channels,
+        )
 
         def _slider_changed(val):
             publish(fig, TimeChange(time=val))
@@ -2484,6 +2496,7 @@ def _slider_changed(val):
                 scaling_time=scaling_time,
                 slider=slider,
                 kwargs=kwargs,
+                images=images,
             ),
         )
         subscribe(
@@ -2532,6 +2545,11 @@ def _slider_changed(val):
             extrapolate=extrapolate,
         )
         _draw_proj_checkbox(None, params)
+        # When the interactive slider is also active, store a reference to
+        # params so that _on_time_change can keep time_idx synchronized and
+        # clear stale contour artists after each ax.clear().
+        if hasattr(fig, "_mne_params"):
+            fig._mne_params["proj_params"] = params
         # This is mostly for testing purposes, but it's also consistent with
         # raw.plot, so maybe not a bad thing in principle either
         from mne.viz._figure import BrowserParams
@@ -2567,13 +2585,35 @@ def _on_time_change(
     scaling_time,
     slider,
     kwargs,
+    images=None,
 ):
     """Handle updating topomap to show a new time."""
+    from ..channels.layout import _merge_ch_data
+
     idx = np.argmin(np.abs(times - event.time))
-    data = func(data[:, idx]).ravel() * scaling
+    # Record the slider's current index in fig._mne_params.  When the
+    # projection callback (_plot_update_evoked_topomap) runs later it needs
+    # this value to apply SSPs at the same time point the slider is showing.
+    _mne_params = getattr(fig, "_mne_params", None)
+    if _mne_params is not None:
+        _mne_params["current_time_idx"] = idx
+        _proj_params = _mne_params.get("proj_params")
+        if _proj_params is not None:
+            _proj_params["contours_"] = []
+        if _mne_params.get("merge_channels", False):
+            ch_type = _mne_params.get("merge_ch_type", "grad")
+            names = _mne_params.get("merge_names", [])
+            data, _ = _merge_ch_data(data[:, idx], ch_type, names)
+        else:
+            data = data[:, idx]
+        data = data.ravel() * scaling
+    else:
+        data = func(data[:, idx]).ravel() * scaling
     ax = fig.axes[0]
     ax.clear()
     im, _ = plot_topomap(data, pos, axes=ax, **kwargs)
+    if images is not None:
+        images[0] = im
     if hasattr(ax, "CB"):
         ax.CB.mappable = im
         _resize_cbar(ax.CB.cbar.ax, 2)