diff --git a/.cache.sqlite b/.cache.sqlite
new file mode 100644
index 0000000..0fd4edd
Binary files /dev/null and b/.cache.sqlite differ
diff --git a/backend/src/data/weather.py b/backend/src/data/weather.py
new file mode 100644
index 0000000..1fbea51
--- /dev/null
+++ b/backend/src/data/weather.py
@@ -0,0 +1,210 @@
+# imports
+from pathlib import Path
+
+import openmeteo_requests
+import pandas as pd
+import requests_cache
+from retry_requests import retry
+
+DATA_DIR = Path(__file__).resolve().parent
+
+
+# Shared Open-Meteo client setup
+cache_session = requests_cache.CachedSession(".cache", expire_after=3600)
+
+retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
+
+openmeteo = openmeteo_requests.Client(session=retry_session)
+
+
+def fetch_longterm_weather(location_name, latitude, longitude):
+    url = "https://climate-api.open-meteo.com/v1/climate"
+
+    params = {
+        "latitude": latitude,
+        "longitude": longitude,
+        "start_date": "2026-01-01",
+        "end_date": "2030-12-31",
+        "models": [
+            "CMCC_CM2_VHR4",
+            "FGOALS_f3_H",
+            "HiRAM_SIT_HR",
+            "MRI_AGCM3_2_S",
+            "EC_Earth3P_HR",
+            "MPI_ESM1_2_XR",
+            "NICAM16_8S",
+        ],
+        "timeformat": "unixtime",
+        "wind_speed_unit": "mph",
+        "temperature_unit": "fahrenheit",
+        "precipitation_unit": "inch",
+        "daily": [
+            "temperature_2m_mean",
+            "wind_speed_10m_mean",
+            "cloud_cover_mean",
+            "precipitation_sum",
+        ],
+    }
+
+    print("\n" + "=" * 60)
+    print(f"LONG-TERM WEATHER: {location_name}")
+    print(f"Latitude: {latitude}")
+    print(f"Longitude: {longitude}")
+    print("=" * 60)
+
+    responses = openmeteo.weather_api(url, params=params)
+
+    all_longterm_data = []
+
+    for response in responses:
+        daily = response.Daily()
+
+        daily_temperature_2m_mean = daily.Variables(0).ValuesAsNumpy()
+
+        daily_wind_speed_10m_mean = daily.Variables(1).ValuesAsNumpy()
+
+        daily_cloud_cover_mean = daily.Variables(2).ValuesAsNumpy()
+
+        daily_precipitation_sum = daily.Variables(3).ValuesAsNumpy()
+
+        daily_data = {
+            "date": pd.date_range(
+                start=pd.to_datetime(daily.Time(), unit="s", utc=True),
+                end=pd.to_datetime(daily.TimeEnd(), unit="s", utc=True),
+                freq=pd.Timedelta(seconds=daily.Interval()),
+                inclusive="left",
+            ).date,
+            "location": location_name,
+            "model_number": response.Model(),
+            "temperature_mean_f": daily_temperature_2m_mean,
+            "wind_speed_mean_mph": daily_wind_speed_10m_mean,
+            "cloud_cover_mean_pct": daily_cloud_cover_mean,
+            "precipitation_sum_in": daily_precipitation_sum,
+        }
+
+        daily_dataframe = pd.DataFrame(daily_data)
+
+        daily_dataframe = daily_dataframe.round(
+            {
+                "temperature_mean_f": 2,
+                "wind_speed_mean_mph": 2,
+                "cloud_cover_mean_pct": 2,
+                "precipitation_sum_in": 3,
+            }
+        )
+
+        print(f"\nModel {response.Model()} sample:")
+
+        print(daily_dataframe.head())
+
+        print("\nMissing values:")
+        print(daily_dataframe.isnull().sum())
+
+        all_longterm_data.append(daily_dataframe)
+
+    combined_longterm = pd.concat(all_longterm_data, ignore_index=True)
+
+    filename = DATA_DIR / f"longterm_weather_{location_name}.csv"
+
+    combined_longterm.to_csv(filename, index=False)
+
+    print(f"\nSaved long-term weather to: {filename}")
+
+    return combined_longterm
+
+
+def fetch_shortterm_weather(location_name, latitude, longitude, timezone):
+    url = "https://api.open-meteo.com/v1/forecast"
+
+    params = {
+        "latitude": latitude,
+        "longitude": longitude,
+        "daily": [
+            "apparent_temperature_max",
+            "precipitation_sum",
+            "uv_index_clear_sky_max",
+            "daylight_duration",
+            "wind_gusts_10m_max",
+        ],
+        "timezone": timezone,
+        "forecast_days": 16,
+    }
+
+    print("\n" + "=" * 60)
+    print(f"SHORT-TERM WEATHER: {location_name}")
+    print(f"Latitude: {latitude}")
+    print(f"Longitude: {longitude}")
+    print("=" * 60)
+
+    responses = openmeteo.weather_api(url, params=params)
+
+    response = responses[0]
+
+    daily = response.Daily()
+
+    daily_apparent_temperature_max = daily.Variables(0).ValuesAsNumpy()
+
+    daily_precipitation_sum = daily.Variables(1).ValuesAsNumpy()
+
+    daily_uv_index_clear_sky_max = daily.Variables(2).ValuesAsNumpy()
+
+    daily_daylight_duration = daily.Variables(3).ValuesAsNumpy()
+
+    daily_wind_gusts_10m_max = daily.Variables(4).ValuesAsNumpy()
+
+    daily_data = {
+        "date": pd.date_range(
+            start=pd.to_datetime(daily.Time(), unit="s", utc=True),
+            end=pd.to_datetime(daily.TimeEnd(), unit="s", utc=True),
+            freq=pd.Timedelta(seconds=daily.Interval()),
+            inclusive="left",
+        ).tz_convert(response.Timezone().decode()),
+        "location": location_name,
+        "apparent_temperature_max": daily_apparent_temperature_max,
+        "precipitation_sum": daily_precipitation_sum,
+        "uv_index_clear_sky_max": daily_uv_index_clear_sky_max,
+        "daylight_duration": daily_daylight_duration,
+        "wind_gusts_10m_max": daily_wind_gusts_10m_max,
+    }
+
+    daily_dataframe = pd.DataFrame(daily_data)
+
+    daily_dataframe = daily_dataframe.round(
+        {
+            "apparent_temperature_max": 2,
+            "precipitation_sum": 3,
+            "uv_index_clear_sky_max": 2,
+            "daylight_duration": 2,
+            "wind_gusts_10m_max": 2,
+        }
+    )
+
+    # Remove incomplete forecast rows
+    daily_dataframe = daily_dataframe.dropna().reset_index(drop=True)
+
+    print("\nShort-term weather sample:")
+    print(daily_dataframe.head())
+
+    print("\nMissing values:")
+    print(daily_dataframe.isnull().sum())
+
+    filename = DATA_DIR / f"shortterm_weather_{location_name}.csv"
+
+    daily_dataframe.to_csv(filename, index=False)
+
+    print(f"\nSaved short-term weather to: {filename}")
+
+    return daily_dataframe
+
+
+if __name__ == "__main__":
+    test_locations = [
+        ("Dublin", 53.331, -6.2489, "Europe/Dublin"),
+        ("Boston", 42.3601, -71.0589, "America/New_York"),
+        ("Madrid", 40.4168, -3.7038, "Europe/Madrid"),
+    ]
+
+    for name, latitude, longitude, timezone in test_locations:
+        fetch_longterm_weather(name, latitude, longitude)
+
+        fetch_shortterm_weather(name, latitude, longitude, timezone)