feat: hgb-v3 — Open-Meteo rain features clear the 12h warning gate

The rolling-origin harness (models/eval_rain.json) showed catchment rain
halving flood-year Brier scores, cutting flood-regime MAE 20-40%, and
extending the hard 2024 leads (+6h -> +11h at P.1, +10h -> +19h at
P.103). Ported: train_all loads the catchment-mean series (use_rain /
--no-rain to opt out; without it bundles train as v2), predict fetches
live rain hourly and passes an empty series on failure so rain-trained
bundles serve with NaN features instead of tripping the feature guard,
and the leader worker persists hourly per-point + catchment-mean rows to
a new openmeteo_rain table.

Regenerated backtest: the 2024 record flood now gets a 13-HOUR WARNING
(alert 04:00 vs 17:00 crossing, river at 2.9m at alert time) — the >=12h
acceptance gate PASSES for the first time. Journey on that crossing:
v1 -18h, v2 +6h, v3 +13h. The marginal 2025 double-crest trades its
artifact +46h latch for a calibrated +2h with zero false alarms. P.1
MAE 4.9/7.2/8.7 cm at 6/12/24h. Docs updated throughout.

src/ml/rain.py

@@ -160,3 +160,60 @@ def serving_series() -> Optional[pd.Series]:
     except Exception as error:
         logger.warning(f"Open-Meteo forecast fetch failed: {error}")
         return None
+
+
+def save_to_db(df: pd.DataFrame, engine, db_type: str) -> int:
+    """Upsert per-point + catchment-mean hourly rain into openmeteo_rain.
+
+    Called by the leader worker's hourly precompute with the live forecast
+    frame, so the DB accumulates both what fell (past rows are the model
+    analysis) and what was forecast (future rows, overwritten as they become
+    past). The ML training path reads Open-Meteo's own archive, not this
+    table — this is for dashboards, SQL analysis, and source independence.
+    """
+    if df is None or df.empty:
+        return 0
+    from sqlalchemy import text
+
+    point_cols = [p[0] for p in CATCHMENT_POINTS]
+    ddl_cols = ", ".join(f"{c} NUMERIC(6,2)" for c in point_cols)
+    ddl = (
+        "CREATE TABLE IF NOT EXISTS openmeteo_rain ("
+        "timestamp TIMESTAMP PRIMARY KEY, "
+        f"{ddl_cols}, catchment_mean NUMERIC(6,2), "
+        "created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP)"
+    )
+    cols = ["timestamp"] + point_cols + ["catchment_mean"]
+    placeholders = ", ".join(f":{c}" for c in cols)
+    updates = ", ".join(
+        f"{c} = "
+        + (f"VALUES({c})" if db_type == "mysql" else f"EXCLUDED.{c}")
+        for c in cols[1:]
+    )
+    if db_type == "mysql":
+        sql = (
+            f"INSERT INTO openmeteo_rain ({', '.join(cols)}) VALUES ({placeholders}) "
+            f"ON DUPLICATE KEY UPDATE {updates}"
+        )
+    else:
+        sql = (
+            f"INSERT INTO openmeteo_rain ({', '.join(cols)}) VALUES ({placeholders}) "
+            f"ON CONFLICT (timestamp) DO UPDATE SET {updates}"
+        )
+    mean = df.mean(axis=1)
+    params = [
+        {
+            "timestamp": ts.to_pydatetime(),
+            **{c: (None if pd.isna(row[c]) else float(row[c])) for c in point_cols},
+            "catchment_mean": None if pd.isna(mean.loc[ts]) else float(mean.loc[ts]),
+        }
+        for ts, row in df.iterrows()
+    ]
+    try:
+        with engine.begin() as conn:
+            conn.execute(text(ddl))
+            conn.execute(text(sql), params)
+        return len(params)
+    except Exception as error:
+        logger.error(f"openmeteo_rain save failed: {error}")
+        return 0