diff --git a/pymc/stats/convergence.py b/pymc/stats/convergence.py
index d32831c8be..7dc880ba0b 100644
--- a/pymc/stats/convergence.py
+++ b/pymc/stats/convergence.py
@@ -144,9 +144,15 @@ def warn_divergences(idata: arviz.InferenceData) -> list[SamplerWarning]:
     n_div = int(diverging.sum())
     if n_div == 0:
         return []
+
+    if n_div == 1:
+        verb, word = "was", "divergence"
+    else:
+        verb, word = "were", "divergences"
+
     warning = SamplerWarning(
         WarningType.DIVERGENCES,
-        f"There were {n_div} divergences after tuning. Increase `target_accept` or reparameterize.",
+        f"There {verb} {n_div} {word} after tuning. Increase `target_accept` or reparameterize.",
         "error",
     )
     return [warning]
diff --git a/tests/stats/test_convergence.py b/tests/stats/test_convergence.py
index 1f7ba44791..52d5c5048c 100644
--- a/tests/stats/test_convergence.py
+++ b/tests/stats/test_convergence.py
@@ -16,19 +16,27 @@
 
 import arviz
 import numpy as np
+import pytest
 
 from pymc.stats import convergence
 
 
-def test_warn_divergences():
+@pytest.mark.parametrize(
+    "diverging, expected_phrase",
+    [
+        pytest.param([1, 0, 1, 0], "were 2 divergences after tuning", id="plural"),
+        pytest.param([1, 0, 0, 0], "was 1 divergence after tuning", id="singular"),
+    ],
+)
+def test_warn_divergences(diverging, expected_phrase):
     idata = arviz.from_dict(
         sample_stats={
-            "diverging": np.array([[1, 0, 1, 0], [0, 0, 0, 0]]).astype(bool),
+            "diverging": np.array([diverging, [0, 0, 0, 0]]).astype(bool),
         }
     )
     warns = convergence.warn_divergences(idata)
     assert len(warns) == 1
-    assert "2 divergences after tuning" in warns[0].message
+    assert expected_phrase in warns[0].message
 
 
 def test_warn_treedepth():