diff --git a/pymc/sampling/forward.py b/pymc/sampling/forward.py
index c07683555a..d997e00466 100644
--- a/pymc/sampling/forward.py
+++ b/pymc/sampling/forward.py
@@ -345,10 +345,13 @@ def draw(
     return [np.stack(v) for v in drawn_values]
 
 
-def observed_dependent_deterministics(model: Model):
+def observed_dependent_deterministics(model: Model, extra_observeds=None):
     """Find deterministics that depend directly on observed variables."""
+    if extra_observeds is None:
+        extra_observeds = []
+
     deterministics = model.deterministics
-    observed_rvs = set(model.observed_RVs)
+    observed_rvs = set(model.observed_RVs + extra_observeds)
     blockers = model.basic_RVs
     return [
         deterministic
@@ -767,6 +770,7 @@ def sample_posterior_predictive(
     if "coords" not in idata_kwargs:
         idata_kwargs["coords"] = {}
     idata: InferenceData | None = None
+    observed_data = None
     stacked_dims = None
     if isinstance(trace, InferenceData):
         _constant_data = getattr(trace, "constant_data", None)
@@ -774,6 +778,7 @@ def sample_posterior_predictive(
             trace_coords.update({str(k): v.data for k, v in _constant_data.coords.items()})
             constant_data.update({str(k): v.data for k, v in _constant_data.items()})
         idata = trace
+        observed_data = trace.get("observed_data", None)
         trace = trace["posterior"]
     if isinstance(trace, xarray.Dataset):
         trace_coords.update({str(k): v.data for k, v in trace.coords.items()})
@@ -816,7 +821,12 @@ def sample_posterior_predictive(
     if var_names is not None:
         vars_ = [model[x] for x in var_names]
     else:
-        vars_ = model.observed_RVs + observed_dependent_deterministics(model)
+        observed_vars = model.observed_RVs
+        if observed_data is not None:
+            observed_vars += [
+                model[x] for x in observed_data if x in model and x not in observed_vars
+            ]
+        vars_ = observed_vars + observed_dependent_deterministics(model, observed_vars)
 
     vars_to_sample = list(get_default_varnames(vars_, include_transformed=False))
 
diff --git a/tests/sampling/test_forward.py b/tests/sampling/test_forward.py
index 404f74a961..9348296297 100644
--- a/tests/sampling/test_forward.py
+++ b/tests/sampling/test_forward.py
@@ -540,6 +540,24 @@ def test_normal_scalar_idata(self):
             ppc = pm.sample_posterior_predictive(idata, return_inferencedata=False)
             assert ppc["a"].shape == (nchains, ndraws)
 
+    def test_external_trace_det(self):
+        with pm.Model() as model:
+            mu = pm.Normal("mu", 0.0, 1.0)
+            a = pm.Normal("a", mu=mu, sigma=1, observed=0.0)
+            b = pm.Deterministic("b", a + 1)
+            trace = pm.sample(tune=50, draws=50, chains=1, compute_convergence_checks=False)
+
+        # test that trace is used in ppc
+        with pm.Model() as model_ppc:
+            mu = pm.Normal("mu", 0.0, 1.0)
+            a = pm.Normal("a", mu=mu, sigma=1)
+            c = pm.Deterministic("c", a + 1)
+
+        ppc = pm.sample_posterior_predictive(
+            trace=trace, model=model_ppc, return_inferencedata=False
+        )
+        assert list(ppc.keys()) == ["a", "c"]
+
     def test_normal_vector(self):
         with pm.Model() as model:
             mu = pm.Normal("mu", 0.0, 1.0)