diff --git a/docs/user-guide/caching-remote-data.md b/docs/user-guide/caching-remote-data.md
new file mode 100644
index 0000000..33cf9ca
--- /dev/null
+++ b/docs/user-guide/caching-remote-data.md
@@ -0,0 +1,139 @@
+# Caching Remote Data
+
+This guide shows how to reduce network requests when you need to read the same remote data multiple times.
+
+## The Problem
+
+When working with cloud-hosted data, every read operation can trigger a network request. If you're accessing the same data repeatedly, this can be slow and wasteful. Repeatedly accessing the same data can happen evening when reading only one file, and also happens when reading a file multiple times.
+
+## The Solution
+
+Wrap your store with [`CachingReadableStore`][obspec_utils.wrappers.CachingReadableStore] to cache files after the first access:
+
+```python exec="on" source="above" session="cache" result="code"
+from obstore.store import S3Store
+from obspec_utils.wrappers import CachingReadableStore
+
+# Create the underlying store
+store = S3Store(
+    bucket="nasanex",
+    aws_region="us-west-2",
+    skip_signature=True,
+)
+
+# Wrap with caching (256 MB default cache size)
+cached_store = CachingReadableStore(store)
+
+# First access fetches from network
+path = "NEX-GDDP/BCSD/rcp85/day/atmos/tasmax/r1i1p1/v1.0/tasmax_day_BCSD_rcp85_r1i1p1_inmcm4_2100.nc"
+data1 = cached_store.get_range(path, start=0, length=1000)
+print(f"After first read: {cached_store.cache_size / 1e6:.1f} MB cached")
+
+# Second access served from cache (no network request)
+data2 = cached_store.get_range(path, start=1000, length=1000)
+print(f"After second read: {cached_store.cache_size / 1e6:.1f} MB cached")
+print(f"Cached files: {len(cached_store.cached_paths)}")
+```
+
+## Sizing Your Cache
+
+Set `max_size` based on your available memory and the files you're working with:
+
+```python exec="on" source="above" session="cache2" result="code"
+from obstore.store import S3Store
+from obspec_utils.wrappers import CachingReadableStore
+
+store = S3Store(
+    bucket="nasanex",
+    aws_region="us-west-2",
+    skip_signature=True,
+)
+
+# 512 MB cache for larger workloads
+cached_store = CachingReadableStore(store, max_size=512 * 1024 * 1024)
+
+# Cache multiple files
+paths = [
+    "NEX-GDDP/BCSD/rcp85/day/atmos/tasmax/r1i1p1/v1.0/tasmax_day_BCSD_rcp85_r1i1p1_inmcm4_2099.nc",
+    "NEX-GDDP/BCSD/rcp85/day/atmos/tasmax/r1i1p1/v1.0/tasmax_day_BCSD_rcp85_r1i1p1_inmcm4_2100.nc",
+]
+
+for path in paths:
+    cached_store.get_range(path, start=0, length=100)
+
+print(f"Cache size: {cached_store.cache_size / 1e6:.1f} MB")
+print(f"Cached files ({len(cached_store.cached_paths)}):")
+for p in cached_store.cached_paths:
+    print(f"  {p.split('/')[-1]}")
+```
+
+When the cache exceeds [`max_size`][obspec_utils.wrappers.CachingReadableStore], the least recently used files are evicted automatically.
+
+## Using with Xarray
+
+Combine caching with readers for xarray workflows:
+
+```python exec="on" source="above" session="cache3" result="code"
+import xarray as xr
+from obstore.store import HTTPStore
+from obspec_utils.wrappers import CachingReadableStore
+from obspec_utils.readers import EagerStoreReader
+
+# Access sample NetCDF files over HTTP
+store = HTTPStore.from_url("https://github.com/pydata/xarray-data/raw/refs/heads/master/")
+cached_store = CachingReadableStore(store)
+
+path = "air_temperature.nc"
+
+# First open: fetches from network
+with EagerStoreReader(cached_store, path) as reader:
+    ds1 = xr.open_dataset(reader, engine="scipy")
+    var_names = list(ds1.data_vars)
+
+print(f"Variables: {var_names}")
+print(f"Cache size after first open: {cached_store.cache_size / 1e6:.2f} MB")
+
+# Second open: served entirely from cache
+with EagerStoreReader(cached_store, path) as reader:
+    ds2 = xr.open_dataset(reader, engine="scipy")
+
+print(f"Cache size after second open: {cached_store.cache_size / 1e6:.2f} MB (unchanged)")
+```
+
+## Cleaning Up
+
+Use the context manager for automatic cleanup, or call [`clear_cache()`][obspec_utils.wrappers.CachingReadableStore.clear_cache] explicitly:
+
+```python exec="on" source="above" session="cache4" result="code"
+from obstore.store import S3Store
+from obspec_utils.wrappers import CachingReadableStore
+
+store = S3Store(
+    bucket="nasanex",
+    aws_region="us-west-2",
+    skip_signature=True,
+)
+path = "NEX-GDDP/BCSD/rcp85/day/atmos/tasmax/r1i1p1/v1.0/tasmax_day_BCSD_rcp85_r1i1p1_inmcm4_2100.nc"
+
+# Option 1: Context manager (cache cleared on exit)
+with CachingReadableStore(store) as cached_store:
+    cached_store.get_range(path, start=0, length=100)
+    print(f"Inside context: {cached_store.cache_size / 1e6:.1f} MB cached")
+# Cache automatically cleared when exiting the context
+print(f"Outside context: {cached_store.cache_size / 1e6:.1f} MB cached")
+
+# Option 2: Explicit cleanup
+cached_store = CachingReadableStore(store)
+cached_store.get_range(path, start=0, length=100)
+print(f"Before clear: {cached_store.cache_size / 1e6:.1f} MB cached")
+cached_store.clear_cache()
+print(f"After clear: {cached_store.cache_size / 1e6:.1f} MB cached")
+```
+
+## When Caching Helps
+
+Caching is most effective when:
+
+- You read the same files multiple times (parsing metadata, then reading data)
+- Multiple operations access overlapping files
+- Files are small enough to fit in your cache budget
diff --git a/mkdocs.yml b/mkdocs.yml
index 6934e50..b46f595 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -17,6 +17,7 @@ nav:
   - "User Guide":
     - "Opening Data with Xarray": "user-guide/opening-data-with-xarray.md"
     - "Finding Files on the Cloud": "user-guide/finding-files.md"
+    - "Minimizing Data Transfer via Caching": "user-guide/caching-remote-data.md"
     - "Debugging Slow Data Access": "user-guide/debugging-data-access.md"
   - "API":
       - Glob: "api/glob.md"