diff --git a/mozaic/core.py b/mozaic/core.py
index e5a1b64..4e7ca3c 100644
--- a/mozaic/core.py
+++ b/mozaic/core.py
@@ -1,3 +1,5 @@
+import warnings
+
 import numpy as np
 import pandas as pd
 
@@ -291,6 +293,19 @@ def _predict_holiday_effects(self):
             .sum()
             .reindex(self.forecast_dates, fill_value=0)
         )
+        # warn if any holiday effects will be clipped at the -0.6 boundary
+        clipped = self.proportional_holiday_effects[self.proportional_holiday_effects < -0.6]
+        if len(clipped) > 0:
+            details = ", ".join(
+                f"{date.strftime('%Y-%m-%d')} ({value:.3f})"
+                for date, value in clipped.items()
+            )
+            warnings.warn(
+                f"Holiday effects clipped at -0.6 boundary for dates: {details}",
+                UserWarning,
+                stacklevel=2,
+            )
+
         # ensure no single-day impact becomes too large
         self.proportional_holiday_effects.clip(lower=-0.6, upper=0, inplace=True)
 
diff --git a/mozaic/models.py b/mozaic/models.py
index 63106bc..ee065dd 100644
--- a/mozaic/models.py
+++ b/mozaic/models.py
@@ -10,9 +10,9 @@ def desktop_forecast_model(historical_data, historical_dates, forecast_dates):
     params = {
         "daily_seasonality": False,
         "weekly_seasonality": True,
-        "yearly_seasonality": False,
+        "yearly_seasonality": True,
         "uncertainty_samples": 1000,
-        "changepoint_range": 0.8,
+        "changepoint_range": 0.7,
         "seasonality_prior_scale": 0.00825,
         "changepoint_prior_scale": 0.15983,
         "growth": "logistic",
@@ -20,15 +20,9 @@ def desktop_forecast_model(historical_data, historical_dates, forecast_dates):
 
     x = historical_data
 
-    # if x.max() >= 10e6:
-    #     params["growth"] = "logistic"
-
     if (x.abs().corr(x.diff().abs()) or 0) > 0.0:
         params["seasonality_mode"] = "multiplicative"
         params["growth"] = "linear"
-        # params["seasonality_prior_scale"] = 20
-        # params["changepoint_prior_scale"] = 0.05
-        # params["growth"] = "logistic"
 
     if (len(x.dropna()) > (365 * 2)) and (
         np.quantile(x.dropna(), 0.5) / (np.quantile(x.dropna(), 0.1) + 1e-8) < 5
@@ -49,25 +43,13 @@ def desktop_forecast_model(historical_data, historical_dates, forecast_dates):
     )
     future = pd.DataFrame({"ds": forecast_dates})
 
-    # if "growth" in params:
-    #     if historical_data.max() >= 10e6:
-    #         cap =  observed["y"].max() * 1.2
-    #         floor = observed["y"].min() * 0.8
-    #         observed["cap"] = cap
-    #         observed["floor"] = floor
-    #         future["cap"] = cap
-    #         future["floor"] = floor
-    #     else:
-    #         cap = observed["y"].max() * 1.2
-    #         floor = 0.0
-    #         observed["cap"] = cap
-    #         observed["floor"] = floor
-    #         future["cap"] = cap
-    #         future["floor"] = floor
-
     if params["growth"] == "logistic":
-        cap = observed["y"].max() * 1.2
-        floor = observed["y"].min() * 0.8
+        cap = observed["y"].tail(366).max() * 1.05
+        if cap > 100e6:
+            floor = observed["y"].tail(366).min() * 1
+        else:
+            floor = observed["y"].tail(366).min() * 0.92
+        
         observed["cap"] = cap
         observed["floor"] = floor
         future["cap"] = cap
@@ -96,13 +78,14 @@ def mobile_forecast_model(historical_data, historical_dates, forecast_dates):
         "weekly_seasonality": True,
         "yearly_seasonality": len(historical_data.dropna()) > (365 * 2),
         "uncertainty_samples": 1000,
-        "changepoint_range": 0.8,
+        "changepoint_range": 0.82,
         "growth": "logistic",
     }
 
     if historical_data.max() >= 1e6:
         params["seasonality_prior_scale"] = 0.1
         params["changepoint_prior_scale"] = 0.1
+        params["growth"] = "linear"
 
     if historical_data.max() <= 2e6:
         params["seasonality_mode"] = "multiplicative"
@@ -118,14 +101,14 @@ def mobile_forecast_model(historical_data, historical_dates, forecast_dates):
 
     if "growth" in params:
         if historical_data.max() >= 10e6:
-            cap = historical_data.max() * 2.0
-            floor = historical_data.min() * 0.8
+            cap = observed["y"].tail(366).max() * 1.10
+            floor = observed["y"].tail(366).min() * 1.05
             observed["cap"] = cap
             observed["floor"] = floor
             future["cap"] = cap
             future["floor"] = floor
         else:
-            cap = historical_data.max() * 2.0
+            cap = historical_data.max() * 1.1
             floor = 0.0
             observed["cap"] = cap
             observed["floor"] = floor