diff --git a/Snakefile b/Snakefile
index 1427850..41b673d 100644
--- a/Snakefile
+++ b/Snakefile
@@ -177,7 +177,7 @@ rule add_colors_to_mlr_model:
         model="results/{lineage}/{geo_resolution}/mlr/initial_MLR_results.json",
         auspice_config="results/{lineage}/auspice_config.json",
     output:
-        model="results/{lineage}/{geo_resolution}/mlr/MLR_results.json",
+        model="results/{lineage}/{geo_resolution}/mlr/MLR_results_with_colors.json",
     params:
         coloring_field=config["coloring_field"],
     shell:
@@ -189,6 +189,34 @@ rule add_colors_to_mlr_model:
             --output {output.model:q}
         """
 
+rule add_global_to_regional_mlr:
+    input:
+        model="results/{lineage}/region/mlr/MLR_results_with_colors.json",
+        regional_weights="config/regional_population_weights.tsv"
+    output:
+        model="results/{lineage}/region/mlr/MLR_results.json"
+    params:
+        aggregate_regions=config.get("aggregate_regions", False)
+    run:
+        if params.aggregate_regions and wildcards.lineage in config["lineages"]:
+            shell("""
+                python scripts/add_global_to_mlr_results.py \
+                    --input-json {input.model} \
+                    --regional-weights {input.regional_weights} \
+                    --output-json {output.model}
+            """)
+        else:
+            shell("cp {input.model} {output.model}")
+
+# For non-region geo_resolutions, just copy the file
+rule finalize_country_mlr:
+    input:
+        model="results/{lineage}/country/mlr/MLR_results_with_colors.json",
+    output:
+        model="results/{lineage}/country/mlr/MLR_results.json",
+    shell:
+        "cp {input.model} {output.model}"
+
 rule parse_mlr_json:
     input:
         model="results/{lineage}/{geo_resolution}/mlr/MLR_results.json",
diff --git a/config/defaults.yaml b/config/defaults.yaml
index 494b2c9..e85b137 100644
--- a/config/defaults.yaml
+++ b/config/defaults.yaml
@@ -33,3 +33,5 @@ prepare_data:
 haplotype_variant_column: "subclade"
 variant: "haplotype"
 coloring_field: "emerging_haplotype"
+
+aggregate_regions: true  # Set to true to add Global region to regional MLR results
diff --git a/config/regional_population_weights.tsv b/config/regional_population_weights.tsv
new file mode 100644
index 0000000..be51d1a
--- /dev/null
+++ b/config/regional_population_weights.tsv
@@ -0,0 +1,11 @@
+region	weight
+Africa	977096.0
+China	1445901.0
+Europe	515136.0
+JapanKorea	124370.0
+NorthAmerica	579891.0
+Oceania	27915.0
+SouthAmerica	431886.0
+SouthAsia	1640015.0
+SoutheastAsia	688728.0
+WestAsia	826100.0
diff --git a/scripts/add_global_to_mlr_results.py b/scripts/add_global_to_mlr_results.py
new file mode 100644
index 0000000..b91b2ca
--- /dev/null
+++ b/scripts/add_global_to_mlr_results.py
@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+# script by Claude Code
+"""
+Add Global region to MLR results by aggregating regional frequencies using population weights
+and copying hierarchical GA values as Global GA values.
+"""
+
+import argparse
+import json
+import pandas as pd
+import numpy as np
+from collections import defaultdict
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--input-json', required=True,
+                        help='Path to input MLR results JSON')
+    parser.add_argument('--regional-weights', required=True,
+                        help='Path to regional population weights TSV')
+    parser.add_argument('--output-json', required=True,
+                        help='Path to output MLR results JSON with Global added')
+    args = parser.parse_args()
+
+    # Read the MLR results JSON
+    with open(args.input_json, 'r') as f:
+        mlr_data = json.load(f)
+
+    # Read regional population weights
+    weights_df = pd.read_csv(args.regional_weights, sep='\t')
+
+    # Map regional names to match MLR data
+    region_name_map = {
+        'Africa': 'Africa',
+        'Europe': 'Europe',
+        'NorthAmerica': 'North America',
+        'SouthAmerica': 'South America',
+        'SoutheastAsia': 'Southeast Asia',
+        'WestAsia': 'West Asia',
+        'Oceania': 'Oceania',
+        'China': 'China',
+        'JapanKorea': 'Japan Korea',
+        'SouthAsia': 'South Asia'
+    }
+
+    # Apply mapping to weights
+    weights_df['mapped_region'] = weights_df['region'].map(region_name_map)
+
+    # Filter to only regions present in the MLR data
+    available_regions = [loc for loc in mlr_data['metadata']['location']
+                        if loc != 'hierarchical']
+    weights_df = weights_df[weights_df['mapped_region'].isin(available_regions)]
+
+    # Normalize weights
+    total_weight = weights_df['weight'].sum()
+    weights_df['normalized_weight'] = weights_df['weight'] / total_weight
+
+    # Create weight lookup
+    weight_lookup = dict(zip(weights_df['mapped_region'], weights_df['normalized_weight']))
+
+    print(f"Using population weights for {len(weight_lookup)} regions:")
+    for region, weight in weight_lookup.items():
+        print(f"  {region:<20} weight: {weight:.4f}")
+
+    # Add "Global" to metadata locations if not present
+    if "Global" not in mlr_data['metadata']['location']:
+        mlr_data['metadata']['location'].append("Global")
+
+    # Collect data by site, date, variant, and ps for aggregation
+    freq_data = defaultdict(lambda: defaultdict(lambda: defaultdict(dict)))
+    raw_freq_data = defaultdict(lambda: defaultdict(lambda: defaultdict(dict)))
+    freq_forecast_data = defaultdict(lambda: defaultdict(lambda: defaultdict(dict)))
+
+    # Process existing data to collect regional values
+    for record in mlr_data['data']:
+        if record['location'] in weight_lookup:
+            if record['site'] == 'freq':
+                key = (record['date'], record['variant'], record['ps'])
+                freq_data[key[0]][key[1]][key[2]][record['location']] = record['value']
+            elif record['site'] == 'raw_freq':
+                key = (record['date'], record['variant'])
+                raw_freq_data[key[0]][key[1]][record['location']] = record['value']
+            elif record['site'] == 'freq_forecast':
+                key = (record['date'], record['variant'], record['ps'])
+                freq_forecast_data[key[0]][key[1]][key[2]][record['location']] = record['value']
+
+    # Create new Global records
+    new_global_records = []
+
+    # Aggregate freq data
+    for date in freq_data:
+        for variant in freq_data[date]:
+            for ps in freq_data[date][variant]:
+                regional_values = freq_data[date][variant][ps]
+                if regional_values:
+                    # Calculate weighted average, skipping None values
+                    valid_regions = [region for region in regional_values
+                                   if region in weight_lookup and regional_values[region] is not None]
+                    if valid_regions:
+                        weighted_sum = sum(regional_values[region] * weight_lookup[region]
+                                         for region in valid_regions)
+                        weight_sum = sum(weight_lookup[region] for region in valid_regions)
+                        if weight_sum > 0:
+                            global_value = weighted_sum / weight_sum
+                            new_global_records.append({
+                                'location': 'Global',
+                                'date': date,
+                                'variant': variant,
+                                'site': 'freq',
+                                'ps': ps,
+                                'value': global_value
+                            })
+
+    # Aggregate raw_freq data
+    for date in raw_freq_data:
+        for variant in raw_freq_data[date]:
+            regional_values = raw_freq_data[date][variant]
+            if regional_values:
+                # Calculate weighted average, skipping None values
+                valid_regions = [region for region in regional_values
+                               if region in weight_lookup and regional_values[region] is not None]
+                if valid_regions:
+                    weighted_sum = sum(regional_values[region] * weight_lookup[region]
+                                     for region in valid_regions)
+                    weight_sum = sum(weight_lookup[region] for region in valid_regions)
+                    if weight_sum > 0:
+                        global_value = weighted_sum / weight_sum
+                        new_global_records.append({
+                            'location': 'Global',
+                            'date': date,
+                            'variant': variant,
+                            'site': 'raw_freq',
+                            'value': global_value
+                        })
+
+    # Aggregate freq_forecast data
+    for date in freq_forecast_data:
+        for variant in freq_forecast_data[date]:
+            for ps in freq_forecast_data[date][variant]:
+                regional_values = freq_forecast_data[date][variant][ps]
+                if regional_values:
+                    # Calculate weighted average, skipping None values
+                    valid_regions = [region for region in regional_values
+                                   if region in weight_lookup and regional_values[region] is not None]
+                    if valid_regions:
+                        weighted_sum = sum(regional_values[region] * weight_lookup[region]
+                                         for region in valid_regions)
+                        weight_sum = sum(weight_lookup[region] for region in valid_regions)
+                        if weight_sum > 0:
+                            global_value = weighted_sum / weight_sum
+                            new_global_records.append({
+                                'location': 'Global',
+                                'date': date,
+                                'variant': variant,
+                                'site': 'freq_forecast',
+                                'ps': ps,
+                                'value': global_value
+                            })
+
+    # Copy hierarchical GA values as Global GA
+    hierarchical_ga_records = [r for r in mlr_data['data']
+                               if r['location'] == 'hierarchical' and r['site'] == 'ga']
+    for record in hierarchical_ga_records:
+        new_record = record.copy()
+        new_record['location'] = 'Global'
+        new_global_records.append(new_record)
+
+    # For smoothed_raw_freq and agg_counts, we'll create empty/zero records for Global
+    # Get unique dates and variants
+    dates = mlr_data['metadata']['dates']
+    variants = mlr_data['metadata']['variants']
+
+    # Add smoothed_raw_freq records (set to 0 or could aggregate if needed)
+    for date in dates:
+        for variant in variants:
+            new_global_records.append({
+                'location': 'Global',
+                'date': date,
+                'variant': variant,
+                'site': 'smoothed_raw_freq',
+                'value': 0.0  # Could aggregate these too if needed
+            })
+
+    # Add agg_counts records (set to 0 or could aggregate if needed)
+    for date in dates:
+        for variant in variants:
+            new_global_records.append({
+                'location': 'Global',
+                'date': date,
+                'variant': variant,
+                'site': 'agg_counts',
+                'value': 0  # Could aggregate these too if needed
+            })
+
+    # Add all new Global records to the data
+    mlr_data['data'].extend(new_global_records)
+
+    # Write output JSON
+    with open(args.output_json, 'w') as f:
+        json.dump(mlr_data, f, indent=2)
+
+    print(f"\nAdded {len(new_global_records)} Global records to MLR results")
+    print(f"Output saved to {args.output_json}")
+
+    # Summary of what was added
+    sites_added = defaultdict(int)
+    for record in new_global_records:
+        sites_added[record['site']] += 1
+
+    print("\nRecords added by site:")
+    for site, count in sorted(sites_added.items()):
+        print(f"  {site:<20} {count:>6} records")
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/generate_regional_population_weights.py b/scripts/generate_regional_population_weights.py
new file mode 100644
index 0000000..222d181
--- /dev/null
+++ b/scripts/generate_regional_population_weights.py
@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+# script by Claude Code
+"""
+Generate regional population weights by aggregating country populations
+according to the geo_regions mapping from nextstrain/seasonal-flu.
+"""
+
+import pandas as pd
+import sys
+
+def main():
+    # Download and read the geo_regions mapping
+    geo_regions_url = "https://raw.githubusercontent.com/nextstrain/seasonal-flu/master/config/geo_regions.tsv"
+    geo_regions = pd.read_csv(geo_regions_url, sep='\t')
+
+    # Download and read the population weights
+    population_url = "https://raw.githubusercontent.com/nextstrain/ncov/master/defaults/population_weights.tsv"
+    population_weights = pd.read_csv(population_url, sep='\t', comment='#')
+
+    # Standardize column names for merging
+    geo_regions.columns = ['country', 'region']
+    population_weights.columns = ['country', 'weight']
+
+    # Merge the dataframes
+    merged = pd.merge(geo_regions, population_weights, on='country', how='left')
+
+    # Check for countries without population data
+    missing_pop = merged[merged['weight'].isna()]
+    if not missing_pop.empty:
+        print("Warning: Countries without population data:", file=sys.stderr)
+        for _, row in missing_pop.iterrows():
+            print(f"  {row['country']} ({row['region']})", file=sys.stderr)
+        print(file=sys.stderr)
+
+    # Remove rows with missing population data
+    merged = merged.dropna(subset=['weight'])
+
+    # Aggregate by region
+    regional_weights = merged.groupby('region')['weight'].sum().reset_index()
+    regional_weights.columns = ['region', 'weight']
+
+    # Sort by region name
+    regional_weights = regional_weights.sort_values('region')
+
+    # Save to TSV
+    output_path = "config/regional_population_weights.tsv"
+    regional_weights.to_csv(output_path, sep='\t', index=False)
+    print(f"Regional population weights saved to {output_path}")
+
+    # Print summary statistics
+    print("\nRegional Population Weights Summary:")
+    print("-" * 50)
+    total_pop = regional_weights['weight'].sum()
+    for _, row in regional_weights.iterrows():
+        percentage = (row['weight'] / total_pop) * 100
+        # Convert weight to millions for readability
+        pop_millions = row['weight'] / 1000
+        print(f"{row['region']:<20} {pop_millions:>10.1f}M ({percentage:>5.1f}%)")
+    print("-" * 50)
+    print(f"{'Total':<20} {total_pop/1000:>10.1f}M (100.0%)")
+
+    # Also check which regions from the results are present
+    expected_regions = ['Africa', 'Europe', 'North America', 'Oceania',
+                       'South America', 'Southeast Asia', 'West Asia']
+
+    print("\nRegions in current workflow:")
+    for region in expected_regions:
+        # Map display names to actual region names in geo_regions.tsv
+        region_map = {
+            'North America': 'NorthAmerica',
+            'South America': 'SouthAmerica',
+            'Southeast Asia': 'SoutheastAsia',
+            'West Asia': 'WestAsia'
+        }
+        actual_region = region_map.get(region, region)
+
+        if actual_region in regional_weights['region'].values:
+            weight = regional_weights[regional_weights['region'] == actual_region]['weight'].iloc[0]
+            print(f"  ✓ {region:<20} (population: {weight/1000:.1f}M)")
+        else:
+            print(f"  ✗ {region:<20} (NOT FOUND in geo_regions)")
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/plot-ga.py b/scripts/plot-ga.py
index c8872dc..f4e46ab 100644
--- a/scripts/plot-ga.py
+++ b/scripts/plot-ga.py
@@ -26,6 +26,10 @@ def plot_ga(input_file, virus, color_file, out_var, out_loc, loc_lst, var_lst, p
 
     df = df[df["location"].isin(loc_filter)]
     df = df[df["variant"].isin(var_filter)]
+
+    # Exclude "Global" from GA plots
+    df = df[df["location"] != "Global"]
+
     base_chart = alt.Chart(df)
 
     # Parse pivot from file