diff --git a/ush/glwu/lead_average.py b/ush/glwu/lead_average.py
index eeafebbbcb..4d899bed7f 100755
--- a/ush/glwu/lead_average.py
+++ b/ush/glwu/lead_average.py
@@ -539,6 +539,7 @@ def plot_lead_average(df: pd.DataFrame, logger: logging.Logger,
     else:
         handles = []
         labels = []
+    n_mods = 0
     for m in range(len(mod_setting_dicts)):
         if model_list[m] in model_colors.model_alias:
             model_plot_name = (
@@ -581,9 +582,10 @@ def plot_lead_average(df: pd.DataFrame, logger: logging.Logger,
                 else:
                     y_vals_metric_min = np.nanmin(y_vals_metric1)
                     y_vals_metric_max = np.nanmax(y_vals_metric1)
-            if m == 0:
+            if n_mods == 0:
                 y_mod_min = y_vals_metric_min
                 y_mod_max = y_vals_metric_max
+                n_mods+=1
             else:
                 if math.isinf(y_mod_min):
                     y_mod_min = y_vals_metric_min
diff --git a/ush/glwu/time_series.py b/ush/glwu/time_series.py
index 9ddece01ee..fc07bb4938 100755
--- a/ush/glwu/time_series.py
+++ b/ush/glwu/time_series.py
@@ -577,6 +577,7 @@ def plot_time_series(df: pd.DataFrame, logger: logging.Logger,
         handles = []
         #labels = []
         labels = [model_list[0].upper()]
+    n_mods = 0    
     for m in range(len(mod_setting_dicts)):
         if model_list[m] in model_colors.model_alias:
             model_plot_name = (
@@ -614,10 +615,12 @@ def plot_time_series(df: pd.DataFrame, logger: logging.Logger,
             else:
                 y_vals_metric_min = np.nanmin(y_vals_metric1)
                 y_vals_metric_max = np.nanmax(y_vals_metric1)
-            if m == 0:
+            
+            if n_mods == 0:
                 y_mod_min = y_vals_metric_min
                 y_mod_max = y_vals_metric_max
                 counts = pivot_counts[str(model_list[m])].values
+                n_mods+=1
             else:
                 y_mod_min = np.nanmin([y_mod_min, y_vals_metric_min])
                 y_mod_max = np.nanmax([y_mod_max, y_vals_metric_max])