diff --git a/superset/utils/pandas_postprocessing/prophet.py b/superset/utils/pandas_postprocessing/prophet.py
index 85d5530937d8..0c71807da109 100644
--- a/superset/utils/pandas_postprocessing/prophet.py
+++ b/superset/utils/pandas_postprocessing/prophet.py
@@ -71,13 +71,21 @@ def _prophet_fit_and_predict(  # pylint: disable=too-many-arguments
     )
     if df["ds"].dt.tz:
         df["ds"] = df["ds"].dt.tz_convert(None)
-    model.fit(df)
-    future = model.make_future_dataframe(periods=periods, freq=freq)
-    forecast = model.predict(future)[["ds", "yhat", "yhat_lower", "yhat_upper"]]
+    try:
+        model.fit(df)
+        future = model.make_future_dataframe(periods=periods, freq=freq)
+        forecast = model.predict(future)[["ds", "yhat", "yhat_lower", "yhat_upper"]]
+    except Exception as ex:  # noqa: BLE001
+        raise InvalidPostProcessingError(
+            _(
+                "Unable to generate forecast: %(error)s",
+                error=str(ex),
+            )
+        ) from ex
     return forecast.join(df.set_index("ds"), on="ds").set_index(["ds"])
 
 
-def prophet(  # pylint: disable=too-many-arguments
+def prophet(  # pylint: disable=too-many-arguments  # noqa: C901
     df: DataFrame,
     time_grain: str,
     periods: int,
@@ -136,6 +144,8 @@ def prophet(  # pylint: disable=too-many-arguments
         raise InvalidPostProcessingError(_("DataFrame must include temporal column"))
     if len(df.columns) < 2:
         raise InvalidPostProcessingError(_("DataFrame include at least one series"))
+    if len(df) < 2:
+        raise InvalidPostProcessingError(_("Forecast requires at least 2 data points"))
 
     target_df = DataFrame()
 
diff --git a/superset/utils/pandas_postprocessing/utils.py b/superset/utils/pandas_postprocessing/utils.py
index 4d6884c8af0e..f41f4a67f3cf 100644
--- a/superset/utils/pandas_postprocessing/utils.py
+++ b/superset/utils/pandas_postprocessing/utils.py
@@ -26,6 +26,8 @@
 from superset.constants import TimeGrain
 from superset.exceptions import InvalidPostProcessingError
 
+_PANDAS_VERSION = tuple(int(x) for x in pd.__version__.split(".")[:2])
+
 NUMPY_FUNCTIONS: dict[str, Callable[..., Any]] = {
     "average": np.average,
     "argmin": np.argmin,
@@ -76,18 +78,18 @@
 )
 
 PROPHET_TIME_GRAIN_MAP: dict[str, str] = {
-    TimeGrain.SECOND: "S",
+    TimeGrain.SECOND: "s",
     TimeGrain.MINUTE: "min",
     TimeGrain.FIVE_MINUTES: "5min",
     TimeGrain.TEN_MINUTES: "10min",
     TimeGrain.FIFTEEN_MINUTES: "15min",
     TimeGrain.THIRTY_MINUTES: "30min",
-    TimeGrain.HOUR: "H",
+    TimeGrain.HOUR: "h",
     TimeGrain.DAY: "D",
     TimeGrain.WEEK: "W",
-    TimeGrain.MONTH: "M",
-    TimeGrain.QUARTER: "Q",
-    TimeGrain.YEAR: "A",
+    TimeGrain.MONTH: "ME" if _PANDAS_VERSION >= (2, 2) else "M",
+    TimeGrain.QUARTER: "QE" if _PANDAS_VERSION >= (2, 2) else "Q",
+    TimeGrain.YEAR: "YE" if _PANDAS_VERSION >= (2, 2) else "A",
     TimeGrain.WEEK_STARTING_SUNDAY: "W-SUN",
     TimeGrain.WEEK_STARTING_MONDAY: "W-MON",
     TimeGrain.WEEK_ENDING_SATURDAY: "W-SAT",
diff --git a/tests/unit_tests/pandas_postprocessing/test_prophet.py b/tests/unit_tests/pandas_postprocessing/test_prophet.py
index 7dacaeff9de1..c87c3790a95c 100644
--- a/tests/unit_tests/pandas_postprocessing/test_prophet.py
+++ b/tests/unit_tests/pandas_postprocessing/test_prophet.py
@@ -16,6 +16,7 @@
 # under the License.
 from datetime import datetime
 from importlib.util import find_spec
+from unittest.mock import patch
 
 import pandas as pd
 import pytest
@@ -186,6 +187,43 @@ def test_prophet_incorrect_time_grain():
         )
 
 
+def test_prophet_insufficient_data():
+    single_row_df = pd.DataFrame(
+        {
+            DTTM_ALIAS: [datetime(2022, 1, 1)],
+            "sales": [100.0],
+        }
+    )
+    with pytest.raises(InvalidPostProcessingError, match="at least 2 data points"):
+        prophet(
+            df=single_row_df,
+            time_grain="P1D",
+            periods=3,
+            confidence_interval=0.9,
+        )
+
+
+def test_prophet_fit_error():
+    if find_spec("prophet") is None:
+        pytest.skip("prophet not installed")
+
+    with patch(
+        "superset.utils.pandas_postprocessing.prophet._prophet_fit_and_predict"
+    ) as mock_fit:
+        mock_fit.side_effect = InvalidPostProcessingError(
+            "Unable to generate forecast: Dataframe has fewer than 2 non-NaN rows."
+        )
+        with pytest.raises(
+            InvalidPostProcessingError, match="Unable to generate forecast"
+        ):
+            prophet(
+                df=prophet_df,
+                time_grain="P1D",
+                periods=3,
+                confidence_interval=0.9,
+            )
+
+
 def test_prophet_uncertainty_lower_bound_can_be_negative_for_negative_series():
     """
     Regression for #21734: when the input series contains negative values,