diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 0000000..8a58697
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,35 @@
+name: Documentation
+
+on:
+  push:
+    branches:
+      - master
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: pages
+  cancel-in-progress: true
+
+jobs:
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/configure-pages@v5
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: 3.x
+      - run: pip install zensical
+      - run: zensical build --clean
+      - uses: actions/upload-pages-artifact@v4
+        with:
+          path: site
+      - uses: actions/deploy-pages@v4
+        id: deployment
diff --git a/.gitignore b/.gitignore
index 440b931..751ff0c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -18,7 +18,8 @@ target
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
+Cargo.lock
 
 # Python
 .venv/
@@ -27,6 +28,7 @@ __pycache__/
 *.so
 *.egg-info/
 dist/
-uv.lock
+*/uv.lock
 .ipynb_checkpoints/
 _version.py
+site/
diff --git a/Cargo.lock b/Cargo.lock
deleted file mode 100644
index 3e6a2e7..0000000
--- a/Cargo.lock
+++ /dev/null
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diff --git a/docs/api-reference.md b/docs/api-reference.md
new file mode 100644
index 0000000..d32ef20
--- /dev/null
+++ b/docs/api-reference.md
@@ -0,0 +1,237 @@
+# API Reference
+
+## xarray Accessors
+
+The primary interface. Registered automatically when you `import extractrs`.
+
+Use the accessor for quick, one-off computations. For multi-variable or multi-file workflows where the grid and polygons stay the same, use the [Low-Level API](#low-level-api) to build the cache once and reuse it.
+
+### Dataset Accessor
+
+::: extractrs.ExtractrsDatasetAccessor
+    options:
+      show_source: false
+      members:
+        - zonal_stats
+
+### DataArray Accessor
+
+::: extractrs.ExtractrsDataArrayAccessor
+    options:
+      show_source: false
+      members:
+        - zonal_stats
+
+## Low-Level API
+
+For workflows that process many timesteps or multiple variables against the same geometries, use the low-level API to build the cache once and apply statistics repeatedly. This avoids redundant cache builds — the accessor rebuilds the cache on every call.
+
+The functions come in four variants:
+
+| | Single timestep (2D array) | Batch (3D array) |
+|---|---|---|
+| **Unweighted** | `apply_stat` | `apply_stat_batch` |
+| **Weighted** | `apply_stat_weights` | `apply_stat_batch_weights` |
+
+### Example
+
+```python
+import numpy as np
+import geopandas as gpd
+import xarray as xr
+from extractrs import build_cache, apply_stat_batch
+
+ds = xr.open_dataset("daymet_tmin_2024.nc")
+basins = gpd.read_file("basins.shp")
+
+# Prepare inputs for build_cache
+wkb_list = [geom.wkb for geom in basins.geometry]
+id_list = basins["COMID"].astype(np.int64).tolist()
+
+# Compute grid extent from cell centers (add half-cell padding for cell edges)
+x = ds["x"].values.astype(np.float64)
+y = ds["y"].values.astype(np.float64)
+dx = abs(float(x[1] - x[0]))
+dy = abs(float(y[1] - y[0]))
+xmin = float(x.min()) - dx / 2
+xmax = float(x.max()) + dx / 2
+ymin = float(y.min()) - dy / 2
+ymax = float(y.max()) + dy / 2
+
+# Build cache once
+cache = build_cache(wkb_list, id_list, xmin, ymin, xmax, ymax, dx, dy)
+
+# Apply to multiple variables using the same cache
+tmin = ds["tmin"].values.astype(np.float64)  # shape: (time, y, x)
+tmax = ds["tmax"].values.astype(np.float64)
+
+nodata = -9999.0
+tmin_means = apply_stat_batch(cache, tmin, nodata, "mean")  # shape: (time, n_basins)
+tmax_means = apply_stat_batch(cache, tmax, nodata, "mean")
+```
+
+!!! note "Grid extent is cell edges, not cell centers"
+
+    `xmin, ymin, xmax, ymax` passed to `build_cache` define the grid extent at **cell edges**. If your coordinates are cell centers (as is typical in xarray), add half a cell width: `xmin = x_centers.min() - dx / 2`. The xarray accessor does this automatically.
+
+### build_cache
+
+```python
+build_cache(
+    wkb_list: list[bytes],
+    id_list: list[int],
+    xmin: float,
+    ymin: float,
+    xmax: float,
+    ymax: float,
+    dx: float,
+    dy: float,
+    method: str = "exact",
+) -> CoverageCache
+```
+
+Build a coverage cache from WKB geometries and grid parameters.
+
+**Parameters:**
+
+- **wkb_list** — List of WKB-encoded geometry bytes (one per polygon). Obtain from a GeoDataFrame with `[geom.wkb for geom in gdf.geometry]`. Supports Polygon and MultiPolygon types.
+- **id_list** — List of integer IDs, same length as `wkb_list`.
+- **xmin, ymin, xmax, ymax** — Grid extent at cell edges (not cell centers).
+- **dx, dy** — Cell size in the x and y directions.
+- **method** — `"exact"` (default) for fractional pixel coverage, `"center"` for cell-center point-in-polygon.
+
+**Returns:** A `CoverageCache` object.
+
+**Raises:** `ValueError` if `wkb_list` and `id_list` have different lengths, or if `method` is not recognized.
+
+### apply_stat
+
+```python
+apply_stat(
+    cache: CoverageCache,
+    raster: numpy.ndarray,  # shape: (rows, cols), dtype: float64
+    nodata: float,
+    stat: str,
+) -> numpy.ndarray  # shape: (n_basins,), dtype: float64
+```
+
+Apply a statistic to a single 2D raster using the coverage cache.
+
+**Parameters:**
+
+- **cache** — `CoverageCache` from `build_cache()`.
+- **raster** — 2D numpy array of raster values. Must be `float64`.
+- **nodata** — Nodata value to exclude from statistics. NaN values are always excluded regardless.
+- **stat** — Statistic name (see [Supported Statistics](#supported-statistics)).
+
+**Returns:** 1D numpy array of length `n_basins` with one result per zone.
+
+### apply_stat_batch
+
+```python
+apply_stat_batch(
+    cache: CoverageCache,
+    raster: numpy.ndarray,  # shape: (time, rows, cols), dtype: float64
+    nodata: float,
+    stat: str,
+) -> numpy.ndarray  # shape: (time, n_basins), dtype: float64
+```
+
+Apply a statistic to a 3D raster (time, rows, cols) using the coverage cache. Processes all timesteps in a single call.
+
+**Parameters:**
+
+- **cache** — `CoverageCache` from `build_cache()`.
+- **raster** — 3D numpy array. Must be `float64`.
+- **nodata** — Nodata value to exclude.
+- **stat** — Statistic name.
+
+**Returns:** 2D numpy array of shape `(time, n_basins)`.
+
+### apply_stat_weights
+
+```python
+apply_stat_weights(
+    cache: CoverageCache,
+    raster: numpy.ndarray,  # shape: (rows, cols), dtype: float64
+    weights: numpy.ndarray,  # shape: (rows, cols), dtype: float64
+    nodata: float,
+    weight_nodata: float,
+    stat: str,
+) -> numpy.ndarray  # shape: (n_basins,), dtype: float64
+```
+
+Apply a weighted statistic to a single 2D raster. Use this for `weighted_mean` or `weighted_sum` with an external weight raster (e.g., cell-area weights for geographic grids).
+
+**Parameters:**
+
+- **cache** — `CoverageCache` from `build_cache()`.
+- **raster** — 2D numpy array of raster values. Must be `float64`.
+- **weights** — 2D numpy array of weight values, same shape as `raster`. Must be `float64`.
+- **nodata** — Nodata value for the raster.
+- **weight_nodata** — Nodata value for the weights array.
+- **stat** — Statistic name.
+
+**Returns:** 1D numpy array of length `n_basins`.
+
+### apply_stat_batch_weights
+
+```python
+apply_stat_batch_weights(
+    cache: CoverageCache,
+    raster: numpy.ndarray,  # shape: (time, rows, cols), dtype: float64
+    weights: numpy.ndarray,  # shape: (rows, cols), dtype: float64
+    nodata: float,
+    weight_nodata: float,
+    stat: str,
+) -> numpy.ndarray  # shape: (time, n_basins), dtype: float64
+```
+
+Apply a weighted statistic to a 3D raster with 2D weights. The same weight array is applied to every timestep.
+
+**Parameters:**
+
+- **cache** — `CoverageCache` from `build_cache()`.
+- **raster** — 3D numpy array. Must be `float64`.
+- **weights** — 2D numpy array, same spatial shape as each raster slice. Must be `float64`.
+- **nodata** — Nodata value for the raster.
+- **weight_nodata** — Nodata value for the weights.
+- **stat** — Statistic name.
+
+**Returns:** 2D numpy array of shape `(time, n_basins)`.
+
+### CoverageCache
+
+An opaque object returned by `build_cache`. Pass it to the `apply_stat*` functions. It stores the precomputed coverage fractions for each polygon against the grid.
+
+- Tied to a specific grid geometry — reuse it only with rasters on the same grid
+- Cannot be serialized to disk or modified after creation
+- Not thread-safe
+
+**Properties:**
+
+- **`n_basins`** (`int`) — Number of basins in the cache. May be less than the number of input geometries if some polygons fall outside the grid extent or have empty geometry.
+
+**Methods:**
+
+- **`ids()`** → `numpy.ndarray[int64]` — Basin IDs in cache order.
+
+## Supported Statistics
+
+| Stat | Formula | Notes |
+|------|---------|-------|
+| `mean` | `sum(x * c) / sum(c)` | Coverage-weighted mean |
+| `sum` | `sum(x * c)` | Coverage-weighted sum |
+| `count` | `sum(c)` | Sum of coverage fractions (returns a float, not an integer cell count) |
+| `min` | `min(x)` where `c > 0` | Minimum value of any covered cell |
+| `max` | `max(x)` where `c > 0` | Maximum value of any covered cell |
+| `variance` | coverage-weighted | Population variance (divides by total weight, not N-1) |
+| `stdev` | coverage-weighted | Population standard deviation |
+| `weighted_mean` | `sum(x * c * w) / sum(c * w)` | Requires `apply_stat_weights` or `apply_stat_batch_weights` |
+| `weighted_sum` | `sum(x * c * w)` | Requires `apply_stat_weights` or `apply_stat_batch_weights` |
+
+Where `x` = cell value, `c` = coverage fraction, `w` = external weight.
+
+Cells with nodata or NaN values are excluded from all statistics. A zone where all covered cells are nodata returns NaN.
+
+`weighted_mean` and `weighted_sum` are only available through the low-level API (`apply_stat_weights`, `apply_stat_batch_weights`). Passing these stat names to the xarray accessor will run with all weights set to 1.0, producing results identical to `mean` and `sum`.
diff --git a/docs/benchmarks.md b/docs/benchmarks.md
new file mode 100644
index 0000000..57b0323
--- /dev/null
+++ b/docs/benchmarks.md
@@ -0,0 +1,64 @@
+# Benchmarks
+
+!!! note
+
+    All benchmark values are taken from the examples/ folder and the included Jupyter Notebooks. 
+
+All benchmarks compare extractrs against [exactextract](https://github.com/isciences/exactextract), the reference implementation for exact zonal statistics.
+
+## CONUS-Scale Benchmark
+
+**Dataset:** Daymet daily minimum temperature (2024), 1km Lambert Conformal Conic grid, 7814 x 8075 cells (~63M cells).
+
+**Polygons:** 225,064 MERIT Pfafstetter level-7 sub-basins covering the contiguous United States.
+
+### Python Package
+
+Median of 10 runs for extractrs (cache pre-built) vs. median of 3 runs for exactextract (fewer runs due to the longer runtime):
+
+| Step | extractrs | exactextract |
+|------|----------:|-------------:|
+| Cache build (one-time) | 8,181 ms | — |
+| Per-timestep stat | 166 ms | 138,392 ms |
+| **Single-timestep total** | **8,347 ms** | **138,392 ms** |
+
+**Single timestep: 17x speedup.** For multi-timestep workflows, the cache cost amortizes away.
+
+**365 daily timesteps (1 year):** extractrs total = 8.2s + 365 x 0.17s = **69s**. exactextract total = 365 x 138s = **14 hours**. Speedup: **733x**.
+
+### Rust Pipeline
+
+| Step | Time |
+|------|-----:|
+| Basin read (shapefile, filtered to CONUS bounding box) | 2.4 s |
+| Raster read (14 GB NetCDF) | 4.9 s |
+| Coverage cache build (one-time) | 12.0 s |
+| Per-day zonal stats | 0.125 s |
+| exactextract per-day equivalent | 140.4 s |
+
+The Python package per-timestep time (166ms) is higher than the Rust pipeline (125ms) due to numpy array marshaling overhead across the PyO3 boundary.
+
+### Why the Cache Matters
+
+exactextract recomputes polygon-cell intersections on every call. extractrs separates the intersection step (cache build) from the aggregation step (apply stat). For multi-timestep workflows — processing a year of daily data across thousands of basins — the intersection geometry never changes. Computing it once and reusing it eliminates redundant work.
+
+## Accuracy
+
+extractrs uses the same polygon-cell intersection algorithm as exactextract. On a validation subset of 82 CONUS basins (spanning diverse sizes from small headwater catchments to large river basins):
+
+- **Pearson R = 1.0**
+- **Maximum absolute difference = 0.0°C** (bit-identical results)
+
+At full CONUS scale (225,064 basins), the Rust pipeline achieves:
+
+- **R = 0.9999999999**
+
+The deviation at scale comes from floating-point accumulation order differences between the two implementations, not algorithmic differences. Both implementations compute the same polygon-cell intersections — the only variation is the order in which partial-cell contributions are summed, which affects the least significant bits of the result.
+
+## Environment
+
+- **CPU:** Single core — no parallelism in the stat application step. The cache build is also single-threaded.
+- **extractrs version:** 0.1.0
+- **exactextract version:** 0.2.0 (PyPI)
+- **Python:** 3.13
+- **Dependencies:** numpy, xarray, geopandas
diff --git a/docs/getting-started.md b/docs/getting-started.md
new file mode 100644
index 0000000..f3c96b7
--- /dev/null
+++ b/docs/getting-started.md
@@ -0,0 +1,199 @@
+# Getting Started
+
+This guide walks through extractrs from installation to saving results.
+
+## Installation
+
+Install extractrs with pip or uv:
+
+=== "pip"
+
+    ```bash
+    pip install extractrs
+    ```
+
+=== "uv"
+
+    ```bash
+    uv add extractrs
+    ```
+
+For CRS mismatch detection, install the `rio` extra (adds [rioxarray](https://corteva.github.io/rioxarray/)):
+
+=== "pip"
+
+    ```bash
+    pip install extractrs[rio]
+    ```
+
+=== "uv"
+
+    ```bash
+    uv add extractrs[rio]
+    ```
+
+## Loading Data
+
+extractrs works with xarray Datasets and DataArrays paired with geopandas GeoDataFrames. The raster and polygons must share the same coordinate reference system (CRS).
+
+```python
+import xarray as xr
+import geopandas as gpd
+import extractrs  # registers the .extrs xarray accessor
+
+# Load a gridded dataset (e.g., Daymet, ERA5, PRISM)
+ds = xr.open_dataset("daymet_tmin_2024.nc")
+
+# Load polygon boundaries (e.g., catchments, counties, watersheds)
+basins = gpd.read_file("merit_basins.shp")
+```
+
+extractrs works with any data xarray can open, including GRIB via the cfgrib engine.
+
+### Data Requirements
+
+**Raster** — The xarray object must have:
+
+- Two spatial dimensions named `y`/`lat`/`latitude` and `x`/`lon`/`longitude` (case-insensitive). Other names like `south_north`, `rlat`, or `easting` are not recognized — rename them first: `ds = ds.rename({"south_north": "y", "west_east": "x"})`
+- Regular spacing (uniform cell size). `dx` and `dy` can differ from each other, but each axis must be uniformly spaced. Curvilinear or Gaussian grids are not supported.
+- Optionally a time dimension named `time` or `t` (case-insensitive). Other names like `valid_time` or `day` must be renamed: `ds = ds.rename({"valid_time": "time"})`
+
+**Polygons** — The GeoDataFrame must contain:
+
+- A geometry column with Polygon or MultiPolygon geometries
+- Optionally an integer ID column (e.g., `COMID`, `basin_id`). The column must contain values castable to `int64`. String identifiers like HUC codes are not supported — map them to integers first.
+
+## Computing Zonal Statistics
+
+### DataArray (single variable)
+
+```python
+result = ds["tmin"].extrs.zonal_stats(basins, stat="mean", id_col="COMID")
+```
+
+Returns an `xarray.DataArray` with spatial dimensions replaced by the zone ID dimension.
+
+### Dataset (multiple variables)
+
+```python
+result = ds.extrs.zonal_stats(
+    basins,
+    stat="mean",
+    id_col="COMID",
+)
+```
+
+This processes **all** data variables in `ds` and returns an `xarray.Dataset`. To select specific variables:
+
+```python
+# Single variable (var takes a string)
+result = ds.extrs.zonal_stats(basins, stat="mean", id_col="COMID", var="tmin")
+
+# Multiple variables (vars takes a list)
+result = ds.extrs.zonal_stats(basins, stat="mean", id_col="COMID", vars=["tmin", "tmax"])
+```
+
+`var` accepts a single string. `vars` accepts a list of strings. Do not pass a list to `var` — use `vars` instead. Passing both `var` and `vars` is not supported.
+
+### Output Shape
+
+| Input shape | Output shape | Return type |
+|-------------|--------------|-------------|
+| `(y, x)` | `(COMID,)` | `DataArray` or `Dataset` |
+| `(time, y, x)` | `(time, COMID)` | `DataArray` or `Dataset` |
+
+When `id_col` is not specified, the output dimension is named `basin` and indexed from 0. Result values are `float64`.
+
+Polygons that fall entirely outside the raster extent or have empty geometry are silently excluded from the output. The output may have fewer zones than the input GeoDataFrame.
+
+!!! warning "Extra dimensions"
+
+    extractrs expects 2D `(y, x)` or 3D `(time, y, x)` arrays. If your data has additional dimensions (e.g., `level`, `ensemble`), select a single slice first: `ds["tmin"].sel(level=1000)`.
+
+## Coverage Methods
+
+The `method` parameter controls how cell coverage is computed. The default is `"exact"`.
+
+```python
+# Exact fractional coverage (default)
+result = ds["tmin"].extrs.zonal_stats(basins, stat="mean", method="exact")
+
+# Cell-center point-in-polygon
+result = ds["tmin"].extrs.zonal_stats(basins, stat="mean", method="center")
+```
+
+`"exact"` computes the true fractional area of each cell covered by the polygon. A cell half-covered by a polygon contributes 50% of its value to the statistic.
+
+`"center"` tests whether each cell's center point falls inside the polygon. Coverage is binary: 1.0 (inside) or 0.0 (outside). This is faster but less accurate — a polygon smaller than a single grid cell may have zero cell centers inside it, producing no coverage and a NaN result.
+
+## Handling NoData
+
+extractrs automatically detects nodata values by checking, in order:
+
+1. `encoding["_FillValue"]` — where xarray stores the original fill value after decoding
+2. `rio.nodata` — if rioxarray is installed
+3. `attrs["_FillValue"]` or `attrs["missing_value"]`
+
+If none of these are found, extractrs falls back to `-9999.0` as a sentinel. Cells matching the nodata value are excluded from all statistics.
+
+NaN values are **always** treated as missing regardless of the nodata setting. For most xarray-decoded NetCDF files, `_FillValue` has already been replaced with NaN, so the nodata detection is a secondary safeguard.
+
+!!! warning "Nodata override"
+
+    There is currently no parameter to override the nodata value. If your data uses a non-standard sentinel that extractrs does not detect, set `_FillValue` in your data's encoding before calling `zonal_stats`: `da.encoding["_FillValue"] = -32768.0`.
+
+## Memory
+
+extractrs loads the full array eagerly into memory. If you open datasets with Dask chunks (`xr.open_dataset(..., chunks={"time": 30})`), the entire array will be materialized when `zonal_stats` is called.
+
+For large datasets, use the low-level API to process in batches:
+
+```python
+from extractrs import build_cache, apply_stat_batch
+import numpy as np
+
+# Build cache once (see API Reference for parameter details)
+# The accessor does this internally — the low-level API exposes it directly
+cache = build_cache(wkb_list, id_list, xmin, ymin, xmax, ymax, dx, dy)
+
+# Process year by year
+for year, group in ds["tmin"].groupby("time.year"):
+    data = group.values.astype(np.float64)
+    result = apply_stat_batch(cache, data, nodata, "mean")
+```
+
+## Saving Results
+
+The output is a standard xarray object. Save it with any format xarray supports:
+
+```python
+# Zarr (recommended for large outputs)
+result.to_zarr("output.zarr")
+
+# NetCDF
+result.to_netcdf("output.nc")
+
+# CSV (for small outputs — use reset_index() for a flat table)
+result.to_dataframe().reset_index().to_csv("output.csv")
+```
+
+## CRS Requirements
+
+The raster and polygon geometries must be in the same CRS. extractrs does not reproject data.
+
+If rioxarray is installed (`pip install extractrs[rio]`), extractrs will:
+
+- **Raise a `ValueError`** if both the raster and GeoDataFrame have a CRS set and they differ
+- **Warn** (`UserWarning`) if the GeoDataFrame has no CRS set
+- **Warn** (`UserWarning`) if you use a geographic CRS (e.g., EPSG:4326) with statistics other than `weighted_mean` or `weighted_sum`, because cell areas vary by latitude in geographic projections
+
+If rioxarray is **not** installed, no CRS checking is performed and mismatched CRS will produce silently wrong results.
+
+### Geographic CRS and Area Weighting
+
+With a **projected CRS** (e.g., Lambert Conformal Conic, UTM), grid cells have approximately equal area and coverage-weighted statistics are correct.
+
+With a **geographic CRS** (e.g., EPSG:4326), cells near the equator cover more ground area than cells near the poles. An unweighted `stat="mean"` treats all cells equally by grid fraction, which over-weights equatorial cells relative to their true area. For geographic grids, either:
+
+- Reproject your data to an equal-area projection before calling `zonal_stats`, or
+- Use `stat="weighted_mean"` with cell-area weights via the low-level API (`apply_stat_weights`)
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..fe2954c
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,76 @@
+# extractrs
+
+Fast exact zonal statistics for xarray — powered by Rust.
+
+extractrs computes coverage-weighted zonal statistics over raster grids using polygon geometries. It provides an alternative to exactextract for common zonal statistics, with a cache-then-apply architecture that eliminates redundant polygon-cell intersection work across timesteps.
+
+## Install
+
+```bash
+pip install extractrs
+```
+
+For CRS mismatch detection, install the `rio` extra (adds [rioxarray](https://corteva.github.io/rioxarray/)):
+
+```bash
+pip install extractrs[rio]
+```
+
+## Quick Example
+
+```python
+import xarray as xr
+import geopandas as gpd
+import extractrs  # registers the .extrs xarray accessor
+
+ds = xr.open_dataset("temperature.nc")
+basins = gpd.read_file("basins.shp")
+
+result = ds["tmin"].extrs.zonal_stats(basins, stat="mean", id_col="COMID")
+```
+
+`result` is an `xarray.DataArray` with the spatial dimensions (y, x) replaced by the zone ID dimension (`COMID`). If the input has a time dimension, the output shape is `(time, COMID)`.
+
+`stat="mean"` computes a coverage-weighted mean — cells partially covered by a polygon contribute proportionally to the result. See [Coverage Methods](#coverage-methods) for details.
+
+## How It Works
+
+1. **Build a coverage cache** — For each polygon, extractrs computes the fractional overlap with every raster cell it touches. This is done once and reused across all timesteps.
+2. **Apply a statistic** — For each timestep, the cached coverage fractions are combined with raster values to produce one result per zone.
+
+The cache is the expensive step (~8s for 225K basins). Once built, applying statistics costs ~166ms per timestep. For a single-timestep workflow, total time is ~8.3s vs. ~138s for exactextract (~17x speedup). The speedup grows with more timesteps as the cache cost is amortized.
+
+## Coverage Methods
+
+extractrs supports two methods for computing cell coverage:
+
+| Method | Description | Use case |
+|--------|-------------|----------|
+| `"exact"` (default) | Fractional pixel coverage via polygon-cell intersection | Production accuracy — uses the same intersection algorithm as exactextract |
+| `"center"` | Binary point-in-polygon test on cell centers (1.0 or 0.0) | Fast approximation — comparable to cell-center-based tools like xarray-spatial |
+
+With `"center"`, a polygon smaller than a single grid cell may have zero cell centers inside it, producing no coverage and a NaN result.
+
+## Supported Statistics
+
+| Statistic | Formula | Description |
+|-----------|---------|-------------|
+| `mean` | `sum(value * coverage) / sum(coverage)` | Coverage-weighted mean |
+| `sum` | `sum(value * coverage)` | Coverage-weighted sum (a cell with value 10 and 50% coverage contributes 5.0) |
+| `count` | `sum(coverage)` | Sum of coverage fractions for cells with valid data (returns a float, not an integer cell count) |
+| `min` | `min(value)` | Minimum value of any touched cell |
+| `max` | `max(value)` | Maximum value of any touched cell |
+| `variance` | coverage-weighted | Population variance (denominator is total coverage weight, not N-1) |
+| `stdev` | coverage-weighted | Population standard deviation |
+| `weighted_mean` | `sum(value * coverage * weight) / sum(coverage * weight)` | Mean weighted by an external weight raster (**low-level API only** — see [API Reference](api-reference.md)) |
+| `weighted_sum` | `sum(value * coverage * weight)` | Sum weighted by an external weight raster (**low-level API only**) |
+
+## Requirements
+
+- Python >= 3.9
+- numpy, xarray, geopandas
+- Raster must be on a **regular (equispaced) grid** — curvilinear, rotated, or Gaussian grids are not supported
+- Spatial dimensions must be named `y`/`lat`/`latitude` and `x`/`lon`/`longitude` (case-insensitive)
+- The `id_col` column must contain **integer values** — string identifiers (e.g., HUC codes) are not supported
+- Geometries and raster must share the same CRS — see [CRS Requirements](getting-started.md#crs-requirements)
+- extractrs loads data eagerly into memory — Dask-backed arrays will be fully materialized when `zonal_stats` is called
diff --git a/extractrs-python/pyproject.toml b/extractrs-python/pyproject.toml
index a90ac06..7baa435 100644
--- a/extractrs-python/pyproject.toml
+++ b/extractrs-python/pyproject.toml
@@ -17,6 +17,7 @@ dependencies = [
 
 [project.optional-dependencies]
 rio = ["rioxarray"]
+docs = ["zensical", "mkdocstrings[python]"]
 
 [tool.maturin]
 python-source = "python"
diff --git a/extractrs-python/uv.lock b/extractrs-python/uv.lock
deleted file mode 100644
index 084a353..0000000
--- a/extractrs-python/uv.lock
+++ /dev/null
@@ -1,1322 +0,0 @@
-version = 1
-revision = 3
-requires-python = ">=3.9"
-resolution-markers = [
-    "python_full_version >= '3.14' and sys_platform == 'win32'",
-    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
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diff --git a/zensical.toml b/zensical.toml
new file mode 100644
index 0000000..b052d56
--- /dev/null
+++ b/zensical.toml
@@ -0,0 +1,44 @@
+[project]
+site_name = "extractrs"
+site_description = "Fast exact zonal statistics for xarray — powered by Rust"
+site_author = "Tadd Bindas"
+site_url = "https://deepgroundwater.com/extractrs"
+repo_url = "https://github.com/DeepGroundwater/extractrs"
+repo_name = "DeepGroundwater/extractrs"
+docs_dir = "docs"
+
+[project.theme]
+language = "en"
+features = [
+    "content.code.copy",
+    "content.code.annotate",
+    "navigation.footer",
+    "navigation.top",
+    "search.highlight",
+]
+
+[project.theme.icon]
+repo = "fontawesome/brands/github"
+
+[[project.theme.palette]]
+scheme = "default"
+primary = "orange"
+toggle.icon = "lucide/sun"
+toggle.name = "Switch to dark mode"
+
+[[project.theme.palette]]
+scheme = "slate"
+primary = "orange"
+accent = "orange"
+toggle.icon = "lucide/moon"
+toggle.name = "Switch to light mode"
+
+[project.plugins.mkdocstrings]
+
+[project.plugins.mkdocstrings.handlers.python]
+paths = ["extractrs-python/python"]
+
+[project.plugins.mkdocstrings.handlers.python.options]
+show_source = true
+show_root_heading = true
+docstring_style = "numpy"