diff --git a/xcdat/temporal.py b/xcdat/temporal.py
index 834eb394..772b442f 100644
--- a/xcdat/temporal.py
+++ b/xcdat/temporal.py
@@ -154,7 +154,13 @@ class TemporalAccessor:
     def __init__(self, dataset: xr.Dataset):
         self._dataset: xr.Dataset = dataset
 
-    def average(self, data_var: str, weighted: bool = True, keep_weights: bool = False):
+    def average(
+        self,
+        data_var: str,
+        weighted: bool = True,
+        keep_weights: bool = False,
+        required_weight_pct: float | None = None,
+    ):
         """
         Returns a Dataset with the average of a data variable and the time
         dimension removed.
@@ -195,6 +201,9 @@ def average(self, data_var: str, weighted: bool = True, keep_weights: bool = Fal
         keep_weights : bool, optional
             If calculating averages using weights, keep the weights in the
             final dataset output, by default False.
+        required_weight_pct : float | None
+            Fraction of data coverage (i.e, weight) needed to return an
+            average value. Value must range from 0 to 1.
 
         Returns
         -------
@@ -230,7 +239,12 @@ def average(self, data_var: str, weighted: bool = True, keep_weights: bool = Fal
         freq = _infer_freq(self._dataset[self.dim])
 
         return self._averager(
-            data_var, "average", freq, weighted=weighted, keep_weights=keep_weights
+            data_var,
+            "average",
+            freq,
+            weighted=weighted,
+            keep_weights=keep_weights,
+            required_weight_pct=required_weight_pct,
         )
 
     def group_average(
@@ -824,10 +838,13 @@ def _averager(
         keep_weights: bool = False,
         reference_period: Optional[Tuple[str, str]] = None,
         season_config: SeasonConfigInput = DEFAULT_SEASON_CONFIG,
+        required_weight_pct: float | None = None,
     ) -> xr.Dataset:
         """Averages a data variable based on the averaging mode and frequency."""
         ds = self._dataset.copy()
-        self._set_arg_attrs(mode, freq, weighted, reference_period, season_config)
+        self._set_arg_attrs(
+            mode, freq, weighted, reference_period, season_config, required_weight_pct
+        )
 
         # Preprocess the dataset based on method argument values.
         ds = self._preprocess_dataset(ds)
@@ -908,6 +925,7 @@ def _set_arg_attrs(
         weighted: bool,
         reference_period: Optional[Tuple[str, str]] = None,
         season_config: SeasonConfigInput = DEFAULT_SEASON_CONFIG,
+        required_weight_pct: float | None = None,
     ):
         """Validates method arguments and sets them as object attributes.
 
@@ -923,6 +941,9 @@ def _set_arg_attrs(
             A dictionary for "season" frequency configurations. If configs for
             predefined seasons are passed, configs for custom seasons are
             ignored and vice versa, by default DEFAULT_SEASON_CONFIG.
+        required_weight_pct : float | None
+            Fraction of data coverage (i.e, weight) needed to return an
+            average value. Value must range from 0 to 1.
 
         Raises
         ------
@@ -950,6 +971,7 @@ def _set_arg_attrs(
         self._mode = mode
         self._freq = freq
         self._weighted = weighted
+        self._required_weight_pct = self._set_required_weight_pct(required_weight_pct)
 
         self._reference_period = None
         if reference_period is not None:
@@ -1208,14 +1230,18 @@ def _group_average(self, ds: xr.Dataset, data_var: str) -> xr.DataArray:
             # achieve this, first broadcast the one-dimensional (temporal
             # dimension) shape of the `weights` DataArray to the
             # multi-dimensional shape of its corresponding data variable.
-            weights, _ = xr.broadcast(self._weights, dv)
-            weights = xr.where(dv.copy().isnull(), 0.0, weights)
+            masked_weights, _ = xr.broadcast(self._weights, dv)
+            masked_weights = xr.where(dv.copy().isnull(), 0.0, masked_weights)
 
             # Perform weighted average using the formula
             # WA = sum(data*weights) / sum(weights). The denominator must be
             # included to take into account zero weight for missing data.
             with xr.set_options(keep_attrs=True):
-                dv = self._group_data(dv).sum() / self._group_data(weights).sum()
+                dv = self._group_data(dv).sum() / self._group_data(masked_weights).sum()
+
+            # Apply the weight threshold using the required percentage (if set).
+            if self._required_weight_pct > 0.0:
+                dv = self._apply_weight_threshold(dv, masked_weights)
 
             # Restore the data variable's name.
             dv.name = data_var
@@ -1300,6 +1326,51 @@ def _get_weights(self, time_bounds: xr.DataArray) -> xr.DataArray:
 
         return weights
 
+    def _set_required_weight_pct(self, required_weight_pct: float | None) -> float:
+        if required_weight_pct is None:
+            required_weight_pct = 0.0
+        elif required_weight_pct < 0.0:
+            raise ValueError(
+                "required_weight argment is less than zero. "
+                "required_weight must be between 0 and 1."
+            )
+        elif required_weight_pct > 1.0:
+            raise ValueError(
+                "required_weight argment is greater than zero. "
+                "required_weight must be between 0 and 1."
+            )
+
+        return required_weight_pct
+
+    def _apply_weight_threshold(
+        self, dv: xr.DataArray, masked_weights: xr.DataArray
+    ) -> xr.DataArray:
+        """Nan out values that don't meet the weight threshold percentage.
+
+        Parameters
+        ----------
+        dv : xr.DataArray
+            The weighted variable.
+        masked_weights : xr.DataArray
+            The weights with missing values masked with 0.0.
+
+        Returns
+        -------
+        xr.DataArray
+            The weighted variable with an weight threshold percentage.
+        """
+        # Sum all weights, including zero for missing values.
+        weight_sum_all = self._weights.sum(dim=self.dim)
+        weight_sum_masked = masked_weights.sum(dim=self.dim)  # type: ignore
+
+        # Get fraction of the available weight.
+        frac = weight_sum_masked / weight_sum_all
+
+        # Nan out values that don't meet specified weight threshold.
+        dv_new = xr.where(frac >= self._required_weight_pct, dv, np.nan)
+
+        return dv_new
+
     def _group_data(self, data_var: xr.DataArray) -> DataArrayGroupBy:
         """Groups a data variable.