diff --git a/design_notes/grouper_objects.md b/design_notes/grouper_objects.md
new file mode 100644
index 00000000000..af42ef2f493
--- /dev/null
+++ b/design_notes/grouper_objects.md
@@ -0,0 +1,240 @@
+# Grouper Objects
+**Author**: Deepak Cherian <deepak@cherian.net>
+**Created**: Nov 21, 2023
+
+## Abstract
+
+I propose the addition of Grouper objects to Xarray's public API so that
+```python
+Dataset.groupby(x=BinGrouper(bins=np.arange(10, 2))))
+```
+is identical to today's syntax:
+```python
+Dataset.groupby_bins("x", bins=np.arange(10, 2))
+```
+
+## Motivation and scope
+
+Xarray's GroupBy API implements the split-apply-combine pattern (Wickham, 2011)[^1], which applies to a very large number of problems: histogramming, compositing, climatological averaging, resampling to a different time frequency, etc.
+The pattern abstracts the following pseudocode:
+```python
+results = []
+for element in unique_labels:
+    subset = ds.sel(x=(ds.x == element))  # split
+    # subset = ds.where(ds.x == element, drop=True)  # alternative
+    result = subset.mean() # apply
+    results.append(result)
+
+xr.concat(results)  # combine
+```
+
+to
+```python
+ds.groupby('x').mean()  # splits, applies, and combines
+```
+
+Efficient vectorized implementations of this pattern are implemented in numpy's [`ufunc.at`](https://numpy.org/doc/stable/reference/generated/numpy.ufunc.at.html), [`ufunc.reduceat`](https://numpy.org/doc/stable/reference/generated/numpy.ufunc.reduceat.html), [`numbagg.grouped`](https://github.com/numbagg/numbagg/blob/main/numbagg/grouped.py), [`numpy_groupies`](https://github.com/ml31415/numpy-groupies), and probably more.
+These vectorized implementations *all* require, as input, an array of integer codes or labels that identify unique elements in the array being grouped over (`'x'` in the example above).
+```python
+import numpy as np
+
+# array to reduce
+a = np.array([1, 1, 1, 1, 2])
+
+# initial value for result
+out = np.zeros((3,), dtype=int)
+
+# integer codes
+labels = np.array([0, 0, 1, 2, 1])
+
+# groupby-reduction
+np.add.at(out, labels, a)
+out  # array([2, 3, 1])
+```
+
+One can 'factorize' or construct such an array of integer codes using `pandas.factorize` or `numpy.unique(..., return_inverse=True)` for categorical arrays; `pandas.cut`, `pandas.qcut`, or `np.digitize` for discretizing continuous variables.
+In practice, since `GroupBy` objects exist, much of complexity in applying the groupby paradigm stems from appropriately factorizing or generating labels for the operation.
+Consider these two examples:
+1. [Bins that vary in a dimension](https://flox.readthedocs.io/en/latest/user-stories/nD-bins.html)
+2. [Overlapping groups](https://flox.readthedocs.io/en/latest/user-stories/overlaps.html)
+3. [Rolling resampling](https://github.com/pydata/xarray/discussions/8361)
+
+Anecdotally, less experienced users commonly resort to the for-loopy implementation illustrated by the pseudocode above when the analysis at hand is not easily expressed using the API presented by Xarray's GroupBy object.
+Xarray's GroupBy API today abstracts away the split, apply, and combine stages but not the "factorize" stage.
+Grouper objects will close the gap.
+
+## Usage and impact
+
+
+Grouper objects
+1. Will abstract useful factorization algorithms, and
+2. Present a natural way to extend GroupBy to grouping by multiple variables: `ds.groupby(x=BinGrouper(...), t=Resampler(freq="M", ...)).mean()`.
+
+In addition, Grouper objects provide a nice interface to add often-requested grouping functionality
+1. A new `SpaceResampler` would allow specifying resampling spatial dimensions.  ([issue](https://github.com/pydata/xarray/issues/4008))
+2. `RollingTimeResampler` would allow rolling-like functionality that understands timestamps ([issue](https://github.com/pydata/xarray/issues/3216))
+3. A `QuantileBinGrouper` to abstract away `pd.cut` ([issue](https://github.com/pydata/xarray/discussions/7110))
+4. A `SeasonGrouper` and `SeasonResampler` would abstract away common annoyances with such calculations today
+   1. Support seasons that span a year-end.
+   2. Only include seasons with complete data coverage.
+   3. Allow grouping over seasons of unequal length
+   4. See [this xcdat discussion](https://github.com/xCDAT/xcdat/issues/416) for a `SeasonGrouper` like functionality:
+   5. Return results with seasons in a sensible order
+5. Weighted grouping ([issue](https://github.com/pydata/xarray/issues/3937))
+   1. Once `IntervalIndex` like objects are supported, `Resampler` groupers can account for interval lengths when resampling.
+
+## Backward Compatibility
+
+Xarray's existing grouping functionality will be exposed using two new Groupers:
+1. `UniqueGrouper` which uses `pandas.factorize`
+2. `BinGrouper` which uses `pandas.cut`
+3. `TimeResampler` which mimics pandas' `.resample`
+
+Grouping by single variables will be unaffected so that `ds.groupby('x')` will be identical to `ds.groupby(x=UniqueGrouper())`.
+Similarly, `ds.groupby_bins('x', bins=np.arange(10, 2))` will be unchanged and identical to `ds.groupby(x=BinGrouper(bins=np.arange(10, 2)))`.
+
+## Detailed description
+
+All Grouper objects will subclass from a Grouper object
+```python
+import abc
+
+class Grouper(abc.ABC):
+    @abc.abstractmethod
+    def factorize(self, by: DataArray):
+        raise NotImplementedError
+
+class CustomGrouper(Grouper):
+    def factorize(self, by: DataArray):
+        ...
+        return codes, group_indices, unique_coord, full_index
+
+    def weights(self, by: DataArray) -> DataArray:
+        ...
+        return weights
+```
+
+### The `factorize` method
+Today, the `factorize` method takes as input the group variable and returns 4 variables (I propose to clean this up below):
+1. `codes`: An array of same shape as the `group` with int dtype. NaNs in `group` are coded by `-1` and ignored later.
+2. `group_indices` is a list of index location of `group` elements that belong to a single group.
+3. `unique_coord` is (usually) a `pandas.Index` object of all unique `group` members present in `group`.
+4. `full_index` is a `pandas.Index` of all `group` members. This is different from `unique_coord` for binning and resampling, where not all groups in the output may be represented in the input `group`. For grouping by a categorical variable e.g. `['a', 'b', 'a', 'c']`, `full_index` and `unique_coord` are identical.
+There is some redundancy here since `unique_coord` is always equal to or a subset of `full_index`.
+We can clean this up (see Implementation below).
+
+### The `weights` method (?)
+
+The proposed `weights` method is optional and unimplemented today.
+Groupers with `weights` will allow composing `weighted` and `groupby` ([issue](https://github.com/pydata/xarray/issues/3937)).
+The `weights` method should return an appropriate array of weights such that the following property is satisfied
+```python
+gb_sum = ds.groupby(by).sum()
+
+weights = CustomGrouper.weights(by)
+weighted_sum = xr.dot(ds, weights)
+
+assert_identical(gb_sum, weighted_sum)
+```
+For example, the boolean weights for `group=np.array(['a', 'b', 'c', 'a', 'a'])` should be
+```
+[[1, 0, 0, 1, 1],
+ [0, 1, 0, 0, 0],
+ [0, 0, 1, 0, 0]]
+```
+This is the boolean "summarization matrix" referred to in the classic Iverson (1980, Section 4.3)[^2] and "nub sieve" in [various APLs](https://aplwiki.com/wiki/Nub_Sieve).
+
+> [!NOTE]
+> We can always construct `weights` automatically using `group_indices` from `factorize`, so this is not a required method.
+
+For a rolling resampling, windowed weights are possible
+```
+[[0.5, 1,    0.5, 0, 0],
+ [0,   0.25, 1,   1, 0],
+ [0,   0,    0,   1, 1]]
+```
+
+### The `preferred_chunks` method (?)
+
+Rechunking support is another optional extension point.
+In `flox` I experimented some with automatically rechunking to make a groupby more parallel-friendly ([example 1](https://flox.readthedocs.io/en/latest/generated/flox.rechunk_for_blockwise.html), [example 2](https://flox.readthedocs.io/en/latest/generated/flox.rechunk_for_cohorts.html)).
+A great example is for resampling-style groupby reductions, for which `codes` might look like
+```
+0001|11122|3333
+```
+where `|` represents chunk boundaries. A simple rechunking to
+```
+000|111122|3333
+```
+would make this resampling reduction an embarassingly parallel blockwise problem.
+
+Similarly consider monthly-mean climatologies for which the month numbers might be
+```
+1 2 3 4 5 | 6 7 8 9 10 | 11 12 1 2 3 | 4 5 6 7 8 | 9 10 11 12 |
+```
+A slight rechunking to
+```
+1 2 3 4 | 5 6 7 8 | 9 10 11 12 | 1 2 3 4 | 5 6 7 8 | 9 10 11 12 |
+```
+allows us to reduce `1, 2, 3, 4` separately from `5,6,7,8` and `9, 10, 11, 12` while still being parallel friendly (see the [flox documentation](https://flox.readthedocs.io/en/latest/implementation.html#method-cohorts) for more).
+
+We could attempt to detect these patterns, or we could just have the Grouper take as input `chunks` and return a tuple of "nice" chunk sizes to rechunk to.
+```python
+def preferred_chunks(self, chunks: ChunksTuple) -> ChunksTuple:
+    pass
+```
+For monthly means, since the period of repetition of labels is 12, the Grouper might choose possible chunk sizes of `((2,),(3,),(4,),(6,))`.
+For resampling, the Grouper could choose to resample to a multiple or an even fraction of the resampling frequency.
+
+## Related work
+
+Pandas has [Grouper objects](https://pandas.pydata.org/docs/reference/api/pandas.Grouper.html#pandas-grouper) that represent the GroupBy instruction.
+However, these objects do not appear to be extension points, unlike the Grouper objects proposed here.
+Instead, Pandas' `ExtensionArray` has a [`factorize`](https://pandas.pydata.org/docs/reference/api/pandas.api.extensions.ExtensionArray.factorize.html) method.
+
+Composing rolling with time resampling is a common workload:
+1. Polars has [`group_by_dynamic`](https://pola-rs.github.io/polars/py-polars/html/reference/dataframe/api/polars.DataFrame.group_by_dynamic.html) which appears to be like the proposed `RollingResampler`.
+2. scikit-downscale provides [`PaddedDOYGrouper`](
+https://github.com/pangeo-data/scikit-downscale/blob/e16944a32b44f774980fa953ea18e29a628c71b8/skdownscale/pointwise_models/groupers.py#L19)
+
+## Implementation Proposal
+
+1. Get rid of `squeeze` [issue](https://github.com/pydata/xarray/issues/2157): [PR](https://github.com/pydata/xarray/pull/8506)
+2. Merge existing two class implementation to a single Grouper class
+   1. This design was implemented in [this PR](https://github.com/pydata/xarray/pull/7206) to account for some annoying data dependencies.
+   2. See [PR](https://github.com/pydata/xarray/pull/8509)
+3. Clean up what's returned by `factorize` methods.
+   1. A solution here might be to have `group_indices: Mapping[int, Sequence[int]]` be a mapping from group index in `full_index` to a sequence of integers.
+   2. Return a `namedtuple` or `dataclass` from existing Grouper factorize methods to facilitate API changes in the future.
+4. Figure out what to pass to `factorize`
+   1. Xarray eagerly reshapes nD variables to 1D. This is an implementation detail we need not expose.
+   2. When grouping by an unindexed variable Xarray passes a `_DummyGroup` object. This seems like something we don't want in the public interface. We could special case "internal" Groupers to preserve the optimizations in `UniqueGrouper`.
+5. Grouper objects will exposed under the `xr.groupers` Namespace. At first these will include `UniqueGrouper`, `BinGrouper`, and `TimeResampler`.
+
+## Alternatives
+
+One major design choice made here was to adopt the syntax `ds.groupby(x=BinGrouper(...))` instead of `ds.groupby(BinGrouper('x', ...))`.
+This allows reuse of Grouper objects, example
+```python
+grouper = BinGrouper(...)
+ds.groupby(x=grouper, y=grouper)
+```
+but requires that all variables being grouped by (`x` and `y` above) are present in Dataset `ds`. This does not seem like a bad requirement.
+Importantly `Grouper` instances will be copied internally so that they can safely cache state that might be shared between `factorize` and `weights`.
+
+Today, it is possible to `ds.groupby(DataArray, ...)`. This syntax will still be supported.
+
+## Discussion
+
+This proposal builds on these discussions:
+1. https://github.com/xarray-contrib/flox/issues/191#issuecomment-1328898836
+2. https://github.com/pydata/xarray/issues/6610
+
+## Copyright
+
+This document has been placed in the public domain.
+
+## References and footnotes
+
+[^1]:  Wickham, H. (2011). The split-apply-combine strategy for data analysis. https://vita.had.co.nz/papers/plyr.html
+[^2]:  Iverson, K.E. (1980). Notation as a tool of thought. Commun. ACM 23, 8 (Aug. 1980), 444–465. https://doi.org/10.1145/358896.358899