diff --git a/examples/xarray_data_run.py b/examples/xarray_data_run.py
new file mode 100644
index 0000000..c1361aa
--- /dev/null
+++ b/examples/xarray_data_run.py
@@ -0,0 +1,124 @@
+#!/usr/bin/env -S uv run --script
+#
+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "omfiles[fsspec,grids,xarray]>=1.2.0",  # x-release-please-version
+#     "matplotlib",
+# ]
+# ///
+
+"""Plot forecast time series for a 3x3 neighbourhood of grid cells."""
+
+import datetime as dt
+
+import fsspec
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+from omfiles.grids import OmGrid
+
+MODEL_DOMAIN = "dwd_icon"
+VARIABLE = "temperature_2m"
+TARGET_LAT = 46.8
+TARGET_LON = 10.8
+GRID_RADIUS = 1  # 1 cell in each direction -> 3x3 neighbourhood
+OUTPUT_PATH = "xarray_data_run_timeseries.png"
+
+# Example URI for a data_run file in the `data_run` S3 bucket.
+# See data organization details: https://github.com/open-meteo/open-data?tab=readme-ov-file#data-organization
+# Note: data_run is retained ~ 3 months. The script uses one file within this period.
+run_time = dt.datetime.now(dt.timezone.utc) - dt.timedelta(days=2)
+s3_uri = (
+    f"s3://openmeteo/data_run/{MODEL_DOMAIN}/{run_time.year}/{run_time.month:02}/{run_time.day:02}/0000Z/{VARIABLE}.om"
+)
+print(f"Using OM file: {s3_uri}")
+
+# Use blockcache so repeated remote reads do not have to fetch the same bytes again.
+backend = fsspec.open(
+    f"blockcache::{s3_uri}",
+    mode="rb",
+    s3={"anon": True, "default_block_size": 65536},
+    blockcache={
+        "cache_storage": "cache",
+        # Data run files do not change, so checking on every access is unnecessary.
+        "check_files": False,
+    },
+)
+
+ds = xr.open_dataset(backend, engine="om")  # type: ignore
+print(ds.data_vars)
+print(ds.attrs)
+
+# time is a coordinate stored in the data_run files as unix timestamp
+print(ds["time"])
+
+# In data_run the variable is encoded in the file_name.
+# The variable itself is not named.
+variable_name = ""
+data = ds[variable_name]
+
+if data.ndim != 3:
+    raise ValueError(f"Expected a 3D variable with shape (lat, lon, time), got {data.shape}")
+
+# Reconstruct the latitude/longitude grid from the projection metadata.
+grid = OmGrid(ds.attrs["crs_wkt"], data.shape[:2])
+lon2d, lat2d = grid.get_meshgrid()
+
+# Find the nearest grid cell to the chosen target location and use the
+# surrounding 3x3 neighbourhood for plotting.
+distance2 = (lat2d - TARGET_LAT) ** 2 + (lon2d - TARGET_LON) ** 2
+center_y, center_x = np.unravel_index(np.argmin(distance2), distance2.shape)
+
+if (
+    center_y - GRID_RADIUS < 0
+    or center_y + GRID_RADIUS >= data.shape[0]
+    or center_x - GRID_RADIUS < 0
+    or center_x + GRID_RADIUS >= data.shape[1]
+):
+    raise ValueError("Target location is too close to the domain boundary for a 3x3 neighbourhood")
+
+# Forecast coordinates may be stored as Unix timestamps. Convert numeric values
+# to UTC datetimes so matplotlib renders readable date labels.
+forecast_dim = data.dims[2]
+forecast_coord = data.coords[forecast_dim]
+
+raw_forecast_times = np.asarray(forecast_coord.values)
+forecast_times = raw_forecast_times.astype("datetime64[s]")
+x_label = "Forecast time (UTC)"
+
+fig, axes = plt.subplots(3, 3, figsize=(14, 10), sharex=True, sharey=True)
+axes = np.asarray(axes)
+
+# Plot one time series for each cell in the 3x3 neighbourhood around the target point.
+for row_offset, y_index in enumerate(range(center_y - GRID_RADIUS, center_y + GRID_RADIUS + 1)):
+    for col_offset, x_index in enumerate(range(center_x - GRID_RADIUS, center_x + GRID_RADIUS + 1)):
+        ax = axes[row_offset, col_offset]
+        series = data.isel({data.dims[0]: y_index, data.dims[1]: x_index}).values
+        cell_lat = lat2d[y_index, x_index]
+        cell_lon = lon2d[y_index, x_index]
+
+        ax.plot(forecast_times, series, linewidth=1.8)
+        ax.grid(True, alpha=0.3)
+        ax.set_title(f"lat={cell_lat:.3f}, lon={cell_lon:.3f}", fontsize=10)
+
+        if np.issubdtype(np.asarray(forecast_times).dtype, np.datetime64):
+            ax.xaxis.set_major_locator(mdates.AutoDateLocator(maxticks=8))
+            ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d\n%H:%M"))
+
+        if row_offset == 2:
+            ax.set_xlabel(x_label)
+        if col_offset == 0:
+            ax.set_ylabel(variable_name)
+
+fig.suptitle(
+    f"{MODEL_DOMAIN} {variable_name} time series for a 3x3 grid neighbourhood\n"
+    f"centered near lat={TARGET_LAT}, lon={TARGET_LON}",
+    fontsize=13,
+    fontweight="bold",
+)
+fig.autofmt_xdate()
+plt.tight_layout()
+plt.savefig(OUTPUT_PATH, dpi=300, bbox_inches="tight")
+print(f"Saved plot to {OUTPUT_PATH}")
diff --git a/examples/spatial_xarray.py b/examples/xarray_spatial.py
similarity index 100%
rename from examples/spatial_xarray.py
rename to examples/xarray_spatial.py
diff --git a/release-please-config.json b/release-please-config.json
index 10d6f66..97066a1 100644
--- a/release-please-config.json
+++ b/release-please-config.json
@@ -13,6 +13,7 @@
     "examples/regrid_ecmwf_ifs_hres_gaussian_O1280_to_0.1_degree.py",
     "examples/regrid_subset_of_projected_domain.py",
     "examples/select_by_coordinates.py",
-    "examples/spatial_xarray.py"
+    "examples/xarray_data_run.py",
+    "examples/xarray_spatial.py"
   ]
 }