diff --git a/openquake/cat/converter.py b/openquake/cat/converter.py
new file mode 100644
index 000000000..e0da71878
--- /dev/null
+++ b/openquake/cat/converter.py
@@ -0,0 +1,142 @@
+# Script Title: GMICEs and IGMCEs of Gallahue and Abrahamson (2023)'s work
+
+# Description:
+# This script implements the methodology described in the paper titled 
+# "New Methodology for Unbiased Ground-Motion Intensity Conversion Equations" 
+# by Gallahue, M., and Abrahamson, N., published in 2023. Ground-motion 
+# intensity conversion equations (GMICEs) and intensity ground-motion 
+# conversion equations (IGMCEs) developed in the work are coded to 
+# obtain PGA and Intensity.
+
+# Reference:
+# Molly Gallahue, Norman Abrahamson; New Methodology for Unbiased Ground‐Motion 
+# Intensity Conversion Equations. Bulletin of the Seismological Society of 
+# America 2023; 113 (3): 1133–1151. doi: https://doi.org/10.1785/0120220224
+
+import numpy as np
+import pandas as pd
+import pathlib
+
+
+COEFFS = {
+    "eq19": {"d1": 2.919, "d2": 0.356, "d3": 0.010, "d4": 1.041, "d5": -0.889, "sigma": 0.566},
+    "eq20": {"h1": 8.622, "h2": 1.230, "h3": 0.056, "h4": -0.568, "sigma": 0.704},
+    "eq22": {"f1": -2.808, "f2": 0.444, "f3": -0.061, "f4": -0.047, "f5": -0.458, "sigma": 0.618},
+    "eq23": {"i1": -6.558, "i2": 0.754, "i3": -0.072, "i4": -0.187, "sigma": 0.667}
+}
+
+
+class GallahueAbrahamson2023Model1:
+    """
+    Ground-motion to intensity conversion (PGA -> Intensity)
+    Equations: 19 or 20
+    Units: pga [g], rhypo [km]
+    Epsilon (ϵ): It can be estimated using the mean ϵ from the disaggregation; 
+    however, if disaggregation results are not available, then ϵ can be approximated 
+    from the slope of the hazard curve at any particular site (Gallahue and Abrahamson, 2023).
+    """
+    def __init__(self, data: np.ndarray):
+        self.data = data
+        self.mint = None
+
+    def get_intensity(self, mode: str = 'eq19', epsilon: float = 0):
+        if mode == 'eq19':
+
+            required = ['pga', 'mag', 'rhypo']
+            self._check_columns(required)
+            
+            c = COEFFS["eq19"]
+            ln_pga = np.log(self.data['pga'])
+            ln_rhypo = np.log(self.data['rhypo'])
+            
+            self.mint = (c["d1"] + c["d2"] * ln_pga + 
+                         c["d3"] * (ln_pga - np.log(0.1))**2 + 
+                         c["d4"] * self.data['mag'] + c["d5"] * ln_rhypo)
+            
+        elif mode == 'eq20':
+            self._check_columns(['pga'])
+            
+            c = COEFFS["eq20"]
+            ln_pga = np.log(self.data['pga'])
+            
+            self.mint = (c["h1"] + c["h2"] * ln_pga + 
+                         c["h3"] * (ln_pga - np.log(0.1))**2 + 
+                         c["h4"] * epsilon)
+        else:
+            raise ValueError("Invalid mode! Choose 'eq19' or 'eq20'.")
+        
+        return self.mint
+
+    def save(self, filename: str):
+        if self.mint is None:
+            raise ValueError("Please run 'get_intensity' before saving.")
+        _save_results(self.data, self.mint, 'intensity', filename)
+
+    def _check_columns(self, required):
+        missing = [col for col in required if col not in self.data.dtype.names]
+        if missing:
+            raise ValueError(f"Missing required columns in structured array: {missing}")
+
+
+class GallahueAbrahamson2023Model2:
+    """
+    Intensity to ground motion conversion (Intensity -> PGA)
+    Equations: 22 or 23
+    Units: pga [g], rjb [km]
+    Epsilon (ϵ): It can be estimated using the mean ϵ from the disaggregation; 
+    however, if disaggregation results are not available, then ϵ can be approximated 
+    from the slope of the hazard curve at any particular site (Gallahue and Abrahamson, 2023).
+    """
+    def __init__(self, data: np.ndarray):
+        self.data = data
+        self.pga = None
+
+    def get_pga(self, mode: str = 'eq22', epsilon: float = 0):
+        if mode == 'eq22':
+            required = ['intensity', 'mag', 'rjb']
+            self._check_columns(required)
+            
+            c = COEFFS["eq22"]
+            ln_rjb = np.log(self.data['rjb'])
+            
+            ln_pga = (c["f1"] + c["f2"] * self.data['intensity'] + 
+                      c["f3"] * (self.data['intensity'] - 6)**2 + 
+                      c["f4"] * self.data['mag'] + c["f5"] * ln_rjb)
+            self.pga = np.exp(ln_pga)
+            
+        elif mode == 'eq23':
+            self._check_columns(['intensity'])
+            
+            c = COEFFS["eq23"]
+            ln_pga = (c["i1"] + c["i2"] * self.data['intensity'] + 
+                      c["i3"] * (self.data['intensity'] - 6)**2 + 
+                      c["i4"] * epsilon)
+            self.pga = np.exp(ln_pga)
+        else:
+            raise ValueError("Invalid mode! Choose 'eq22' or 'eq23'.")
+            
+        return self.pga
+
+    def save(self, filename: str):
+        if self.pga is None:
+            raise ValueError("Please run 'get_pga' before saving.")
+        _save_results(self.data, self.pga, 'pga', filename)
+
+    def _check_columns(self, required):
+        missing = [col for col in required if col not in self.data.dtype.names]
+        if missing:
+            raise ValueError(f"Missing required columns in structured array: {missing}")
+
+
+def _save_results(data: np.ndarray, result_array: np.ndarray, result_name: str, filename: str):
+    path = pathlib.Path(filename)
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    keys = data.dtype.names
+    tmp = {k: data[k] for k in keys}
+    tmp[result_name] = result_array
+    
+    df = pd.DataFrame(tmp)
+    df = df.round(5)
+    df.to_csv(path, index=False)
+    print(f"Done! Saved to '{filename}'")
diff --git a/openquake/cat/tests/converter_test.py b/openquake/cat/tests/converter_test.py
new file mode 100644
index 000000000..e828d2126
--- /dev/null
+++ b/openquake/cat/tests/converter_test.py
@@ -0,0 +1,102 @@
+import os
+import unittest
+import pandas as pd
+import numpy as np
+import tempfile
+import shutil
+
+from openquake.cat.converter import GallahueAbrahamson2023Model1, GallahueAbrahamson2023Model2
+
+class TestGallahueAbrahamson2023(unittest.TestCase):
+
+    def setUp(self):
+        """Initialize temporary directory and create structured array for testing."""
+        self.test_dir = tempfile.mkdtemp()
+        
+        # Sample data for testing
+        self.sample_data = pd.DataFrame({
+            "pga": [0.07, 0.20, 0.46],       # g
+            "mag": [6.0, 6.5, 7.3],
+            "rhypo": [10.0, 50.0, 100.0],    # km
+            "rjb": [9.0, 45.0, 95.0],        # km
+            "intensity": [3.9, 5.8, 8.0]
+        })
+        
+        self.structured_data = self.sample_data.to_records(index=False)
+
+    def tearDown(self):
+        shutil.rmtree(self.test_dir)
+        if os.path.exists("output"):
+            shutil.rmtree("output")
+
+    def test_model1_eq19_success(self):
+        """Testing if equation 19 calculates intensity and saves correctly."""
+        model = GallahueAbrahamson2023Model1(self.structured_data)
+        model.get_intensity(mode='eq19')
+        
+        output_path = os.path.join("output", "test_gmice.csv")
+        model.save(output_path)
+        
+        self.assertTrue(os.path.exists(output_path))
+        df_res = pd.read_csv(output_path)
+        
+        # Checking if the result column 'intensity' exists
+        self.assertIn("intensity", df_res.columns)
+        self.assertEqual(len(df_res), 3)
+
+    def test_model1_eq20_with_epsilon(self):
+        """Testing if equation 20 handles epsilon shifts correctly."""
+        model = GallahueAbrahamson2023Model1(self.structured_data)
+
+        # Calculation with epsilon=0 and epsilon=1
+        mean_results = model.get_intensity(mode='eq20', epsilon=0).copy()
+        eps_results = model.get_intensity(mode='eq20', epsilon=1)
+        
+        # Eq 20 has h4 = -0.568. If epsilon=1, intensity should be lower than the mean.
+        self.assertTrue(np.all(eps_results < mean_results))
+
+    def test_model1_missing_column_error(self):
+        bad_dt = np.dtype([('pga', 'f8'), ('mag', 'f8')])
+        bad_data = np.array([(0.1, 6.0)], dtype=bad_dt)
+        
+        model = GallahueAbrahamson2023Model1(bad_data)
+        with self.assertRaises(ValueError) as cm:
+            model.get_intensity(mode='eq19')
+        self.assertIn("Missing required columns", str(cm.exception))
+
+    def test_model2_eq22_success(self):
+        """Testing if equation 22 calculates PGA and saves correctly."""
+        model = GallahueAbrahamson2023Model2(self.structured_data)
+        model.get_pga(mode='eq22')
+        
+        output_path = os.path.join("output", "test_igmce.csv")
+        model.save(output_path)
+        
+        self.assertTrue(os.path.exists(output_path))
+        df_res = pd.read_csv(output_path)
+        self.assertIn("pga", df_res.columns)
+        self.assertTrue((df_res["pga"] > 0).all())
+
+    def test_model2_eq23_with_epsilon(self):
+        """Verifies equation 23 shifts PGA when epsilon is changed."""
+        model = GallahueAbrahamson2023Model2(self.structured_data)
+        
+        pga_mean = model.get_pga(mode='eq23', epsilon=0).copy()
+        pga_eps = model.get_pga(mode='eq23', epsilon=1)
+        
+        # Confirming results are different
+        self.assertFalse(np.array_equal(pga_mean, pga_eps))
+
+    def test_invalid_mode_error(self):
+        """Verifies that an invalid mode string raises a ValueError."""
+        model = GallahueAbrahamson2023Model1(self.structured_data)
+        with self.assertRaises(ValueError) as cm:
+            model.get_intensity(mode='wrong_mode')
+        self.assertIn("Invalid mode", str(cm.exception))
+
+    def test_save_before_calculation_error(self):
+        """Verifies that saving before calculation triggers an error."""
+        model = GallahueAbrahamson2023Model2(self.structured_data)
+        with self.assertRaises(ValueError) as cm:
+            model.save("failure.csv")
+        self.assertIn("run 'get_pga' before saving", str(cm.exception))