diff --git a/seaborn/relational.py b/seaborn/relational.py
index bb872887a1..8d0d856021 100644
--- a/seaborn/relational.py
+++ b/seaborn/relational.py
@@ -792,17 +792,18 @@ def relplot(
 
     # Add the grid semantics onto the plotter
     grid_variables = dict(
-        x=x, y=y, row=row, col=col,
-        hue=hue, size=size, style=style,
+        x=x, y=y, row=row, col=col, hue=hue, size=size, style=style,
     )
     if kind == "line":
-        grid_variables["units"] = units
+        grid_variables.update(units=units, weights=weights)
     p.assign_variables(data, grid_variables)
 
     # Define the named variables for plotting on each facet
     # Rename the variables with a leading underscore to avoid
     # collisions with faceting variable names
     plot_variables = {v: f"_{v}" for v in variables}
+    if "weight" in plot_variables:
+        plot_variables["weights"] = plot_variables.pop("weight")
     plot_kws.update(plot_variables)
 
     # Pass the row/col variables to FacetGrid with their original
@@ -930,6 +931,10 @@ def relplot(
     Grouping variable that will produce elements with different styles.
     Can have a numeric dtype but will always be treated as categorical.
 {params.rel.units}
+weights : vector or key in `data`
+    Data values or column used to compute weighted estimation.
+    Note that use of weights currently limits the choice of statistics
+    to a 'mean' estimator and 'ci' errorbar.
 {params.facets.rowcol}
 {params.facets.col_wrap}
 row_order, col_order : lists of strings
diff --git a/tests/test_relational.py b/tests/test_relational.py
index 2f5eda1dc6..06d0860a38 100644
--- a/tests/test_relational.py
+++ b/tests/test_relational.py
@@ -578,6 +578,15 @@ def test_relplot_styles(self, long_df):
             expected_paths = [paths[val] for val in grp_df["a"]]
             assert self.paths_equal(points.get_paths(), expected_paths)
 
+    def test_relplot_weighted_estimator(self, long_df):
+
+        g = relplot(data=long_df, x="a", y="y", weights="x", kind="line")
+        ydata = g.ax.lines[0].get_ydata()
+        for i, label in enumerate(g.ax.get_xticklabels()):
+            pos_df = long_df[long_df["a"] == label.get_text()]
+            expected = np.average(pos_df["y"], weights=pos_df["x"])
+            assert ydata[i] == pytest.approx(expected)
+
     def test_relplot_stringy_numerics(self, long_df):
 
         long_df["x_str"] = long_df["x"].astype(str)
@@ -1063,8 +1072,8 @@ def test_weights(self, long_df):
 
         ax = lineplot(long_df, x="a", y="y", weights="x")
         vals = ax.lines[0].get_ydata()
-        for i, a in enumerate(ax.get_xticklabels()):
-            pos_df = long_df.loc[long_df["a"] == a.get_text()]
+        for i, label in enumerate(ax.get_xticklabels()):
+            pos_df = long_df.loc[long_df["a"] == label.get_text()]
             expected = np.average(pos_df["y"], weights=pos_df["x"])
             assert vals[i] == pytest.approx(expected)