diff --git a/.flake8 b/.flake8
index 6aa94ce03..db12200fe 100644
--- a/.flake8
+++ b/.flake8
@@ -10,6 +10,8 @@ ignore =
 	D,
 	E,
 	F,
+	RST210,
+	RST213,
 	W503
 per-file-ignores =
 	xclim/core/locales.py:RST399
diff --git a/xclim/analog.py b/xclim/analog.py
index 35d7cc854..239856463 100644
--- a/xclim/analog.py
+++ b/xclim/analog.py
@@ -46,7 +46,7 @@ def spatial_analogs(
         The dimension over which the *distributions* are constructed. This can be a multi-index dimension.
     method : {'seuclidean', 'nearest_neighbor', 'zech_aslan', 'kolmogorov_smirnov', 'friedman_rafsky', 'kldiv'}
         Which method to use when computing the dissimilarity statistic.
-    \*\*kwargs : dict
+    **kwargs : dict
         Any other parameter passed directly to the dissimilarity method.
 
     Returns
diff --git a/xclim/core/bootstrapping.py b/xclim/core/bootstrapping.py
index 00a2a946a..b7dab3353 100644
--- a/xclim/core/bootstrapping.py
+++ b/xclim/core/bootstrapping.py
@@ -95,7 +95,7 @@ def bootstrap_func(compute_index_func: Callable, **kwargs) -> xarray.DataArray:
     ----------
     compute_index_func : Callable
         Index function.
-    \*\*kwargs : dict
+    **kwargs : dict
         Arguments to `func`.
 
     Returns
diff --git a/xclim/core/formatting.py b/xclim/core/formatting.py
index 428094559..e76e6502c 100644
--- a/xclim/core/formatting.py
+++ b/xclim/core/formatting.py
@@ -86,9 +86,9 @@ def format(self, format_string: str, /, *args: Any, **kwargs: dict) -> str:
         ----------
         format_string : str
             The string to format.
-        \*args : Any
+        *args : Any
             Arguments to format.
-        \*\*kwargs : dict
+        **kwargs : dict
             Keyword arguments to format.
 
         Returns
@@ -352,7 +352,7 @@ def merge_attributes(
     ----------
     attribute : str
         The attribute to merge.
-    \*inputs_list : xr.DataArray or xr.Dataset
+    *inputs_list : xr.DataArray or xr.Dataset
         The datasets or variables that were used to produce the new object.
         Inputs given that way will be prefixed by their `name` attribute if available.
     new_line : str
@@ -361,7 +361,7 @@ def merge_attributes(
     missing_str : str
         A string that is printed if an input doesn't have the attribute. Defaults to None, in which
         case the input is simply skipped.
-    \*\*inputs_kws : xr.DataArray or xr.Dataset
+    **inputs_kws : xr.DataArray or xr.Dataset
         Mapping from names to the datasets or variables that were used to produce the new object.
         Inputs given that way will be prefixes by the passed name.
 
@@ -405,12 +405,12 @@ def update_history(
     ----------
     hist_str : str
         The string describing what has been done on the data.
-    \*inputs_list : xr.DataArray or xr.Dataset
+    *inputs_list : xr.DataArray or xr.Dataset
         The datasets or variables that were used to produce the new object.
         Inputs given that way will be prefixed by their "name" attribute if available.
     new_name : str, optional
         The name of the newly created variable or dataset to prefix hist_msg.
-    \*\*inputs_kws : xr.DataArray or xr.Dataset
+    **inputs_kws : xr.DataArray or xr.Dataset
         Mapping from names to the datasets or variables that were used to produce the new object.
         Inputs given that way will be prefixes by the passed name.
 
@@ -511,9 +511,9 @@ def gen_call_string(funcname: str, *args, **kwargs) -> str:
     ----------
     funcname : str
         Name of the function.
-    \*args : Any
+    *args : Any
         Arguments given to the function.
-    \*\*kwargs : dict
+    **kwargs : dict
         Keyword arguments given to the function.
 
     Returns
diff --git a/xclim/core/indicator.py b/xclim/core/indicator.py
index 4259ba397..0ce7e8e2e 100644
--- a/xclim/core/indicator.py
+++ b/xclim/core/indicator.py
@@ -547,7 +547,7 @@ def _parse_indice(compute, passed_parameters):  # noqa: F841
         # Remove the \\* symbols from the parameter names
         _sanitized_params_dict = {}
         for param in params_dict.keys():
-            _sanitized_params_dict[param.replace("\\*", "")] = params_dict[param]
+            _sanitized_params_dict[param.replace("*", "")] = params_dict[param]
         params_dict = _sanitized_params_dict
 
         # Check that the `Parameters` section of the docstring does not include parameters
@@ -1353,7 +1353,7 @@ def cfcheck(self, **das) -> None:
 
         Parameters
         ----------
-        \*\*das : dict
+        **das : dict
             A dictionary of DataArrays to check.
         """
         for varname, vardata in das.items():
@@ -1377,7 +1377,7 @@ def datacheck(self, **das) -> None:
 
         Parameters
         ----------
-        \*\*das : dict
+        **das : dict
             A dictionary of DataArrays to check.
 
         Raises
diff --git a/xclim/core/locales.py b/xclim/core/locales.py
index fd6c15c64..ff086c407 100644
--- a/xclim/core/locales.py
+++ b/xclim/core/locales.py
@@ -157,7 +157,7 @@ def get_local_attrs(
         Indicator's class name, usually the same as in `xc.core.indicator.registry`.
         If multiple names are passed, the attrs from each indicator are merged,
         with the highest priority set to the first name.
-    \*locales : str or tuple of str
+    *locales : str or tuple of str
         IETF language tag or a tuple of the language tag and a translation dict, or a tuple of the language tag
         and a path to a json file defining translation of attributes.
     names : sequence of str, optional
diff --git a/xclim/core/missing.py b/xclim/core/missing.py
index 65ae8536f..ed644b3ce 100644
--- a/xclim/core/missing.py
+++ b/xclim/core/missing.py
@@ -70,7 +70,7 @@ class MissingBase:
         Resampling frequency.
     src_timestep : str, Optional
         The expected input frequency. If not given, it will be inferred from the input array.
-    \*\*indexer : Indexer
+    **indexer : Indexer
         Time attribute and values over which to subset the array. For example, use season='DJF' to select winter
         values, month=1 to select January, or month=[6,7,8] to select summer months.
         If not indexer is given, all values are considered.
@@ -153,7 +153,7 @@ def is_null(
             Input data.
         freq : str
             Resampling frequency, from the periods defined in :ref:`timeseries.resampling`.
-        \*\*indexer : {dim: indexer}, optional
+        **indexer : {dim: indexer}, optional
             The time attribute and values over which to subset the array. For example, use season='DJF' to select winter
             values, month=1 to select January, or month=[6,7,8] to select summer months.
 
@@ -191,7 +191,7 @@ def prepare(
             Resampling frequency, from the periods defined in :ref:`timeseries.resampling`.
         src_timestep : str
             Expected input frequency, from the periods defined in :ref:`timeseries.resampling`.
-        \*\*indexer : {dim: indexer}, optional
+        **indexer : {dim: indexer}, optional
             Time attribute and values over which to subset the array. For example, use season='DJF' to select winter
             values, month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given,
             all values are considered.
@@ -279,7 +279,7 @@ def validate(**kwargs) -> bool:
 
         Parameters
         ----------
-        \*\*kwargs : dict
+        **kwargs : dict
             Options arguments.
 
         Returns
@@ -312,7 +312,7 @@ class MissingAny(MissingBase):
         Resampling frequency.
     src_timestep : {"D", "h", "M"}
         Expected input frequency.
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Time attribute and values over which to subset the array. For example, use season='DJF' to select winter
         values, month=1 to select January, or month=[6,7,8] to select summer months.
         If not indexer is given, all values are considered.
@@ -337,7 +337,7 @@ def is_missing(
             Boolean array indicating which values are null.
         count : xr.DataArray
             Array of expected number of valid values.
-        \*\*kwargs : dict
+        **kwargs : dict
             Additional arguments.
 
         Returns
@@ -375,7 +375,7 @@ class MissingWMO(MissingAny):
         Number of consecutive missing values per month that should not be exceeded.
     src_timestep : {"D"}
         Expected input frequency. Only daily values are supported.
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Time attribute and values over which to subset the array. For example, use season='DJF' to select winter
         Time attribute and values over which to subset the array. For example, use season='DJF' to select winter
         values, month=1 to select January, or month=[6,7,8] to select summer months.
@@ -480,7 +480,7 @@ class MissingPct(MissingBase):
         Fraction of missing values that are tolerated [0,1].
     src_timestep : {"D", "h"}
         Expected input frequency.
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Time attribute and values over which to subset the array. For example, use season='DJF' to select winter values,
         month=1 to select January, or month=[6,7,8] to select summer months.
         If not indexer is given, all values are considered.
diff --git a/xclim/core/options.py b/xclim/core/options.py
index 4993b0d75..20547b9b7 100644
--- a/xclim/core/options.py
+++ b/xclim/core/options.py
@@ -133,9 +133,9 @@ def run_check(func, option, *args, **kwargs):
         Function to run.
     option : str
         Option to use.
-    \*args : tuple
+    *args : tuple
         Positional arguments to pass to the function.
-    \*\*kwargs : dict
+    **kwargs : dict
         Keyword arguments to pass to the function.
 
     Raises
diff --git a/xclim/core/units.py b/xclim/core/units.py
index fa7bc15fb..0e3237d05 100644
--- a/xclim/core/units.py
+++ b/xclim/core/units.py
@@ -1295,7 +1295,7 @@ def declare_relative_units(**units_by_name) -> Callable:
 
     Parameters
     ----------
-    \*\*units_by_name : dict
+    **units_by_name : dict
         Mapping from the input parameter names to dimensions relative to other parameters.
         The dimensions can be a single parameter name as `<other_var>` or more complex expressions,
         such as `<other_var> * [time]`.
@@ -1407,7 +1407,7 @@ def declare_units(**units_by_name) -> Callable:
 
     Parameters
     ----------
-    \*\*units_by_name : dict
+    **units_by_name : dict
         Mapping from the input parameter names to their units or dimensionality ("[...]").
         If this decorates a function previously decorated with :py:func:`declare_relative_units`,
         the relative unit declarations are made absolute with the information passed here.
diff --git a/xclim/core/utils.py b/xclim/core/utils.py
index 383a4f8a7..612f6a69b 100644
--- a/xclim/core/utils.py
+++ b/xclim/core/utils.py
@@ -130,7 +130,7 @@ def ensure_chunk_size(da: xr.DataArray, **minchunks: int) -> xr.DataArray:
     ----------
     da : xr.DataArray
         The input DataArray, with or without the dask backend. Does nothing when passed a non-dask array.
-    \*\*minchunks : dict[str, int]
+    **minchunks : dict[str, int]
         A kwarg mapping from dimension name to minimum chunk size.
         Pass -1 to force a single chunk along that dimension.
 
@@ -178,7 +178,7 @@ def uses_dask(*das: xr.DataArray | xr.Dataset) -> bool:
 
     Parameters
     ----------
-    \*das : xr.DataArray or xr.Dataset
+    *das : xr.DataArray or xr.Dataset
         DataArrays or Datasets to check.
 
     Returns
diff --git a/xclim/ensembles/_base.py b/xclim/ensembles/_base.py
index 25990657b..823d10b0b 100644
--- a/xclim/ensembles/_base.py
+++ b/xclim/ensembles/_base.py
@@ -72,7 +72,7 @@ def create_ensemble(
     cal_kwargs : dict, optional
         Additional arguments to pass to py:func:`xclim.core.calendar.convert_calendar`.
         For conversions involving '360_day', the align_on='date' option is used by default.
-    \*\*xr_kwargs : dict
+    **xr_kwargs : dict
         Any keyword arguments to be given to `xr.open_dataset` when opening the files (or to `xr.open_mfdataset` if `multifile` is True).
 
     Returns
@@ -410,7 +410,7 @@ def _ens_align_datasets(
         the align_on='date' option is used.
         See :py:func:`xclim.core.calendar.convert_calendar`.
         'default' is the standard calendar using np.datetime64 objects.
-    \*\*xr_kwargs
+    **xr_kwargs
         Any keyword arguments to be given to xarray when opening the files.
 
     Returns
diff --git a/xclim/ensembles/_filters.py b/xclim/ensembles/_filters.py
index 4f2d0c475..8a72db7a4 100644
--- a/xclim/ensembles/_filters.py
+++ b/xclim/ensembles/_filters.py
@@ -16,7 +16,7 @@ def _concat_hist(da: xr.DataArray, **hist) -> xr.DataArray:
     ----------
     da : xr.DataArray
         Input data where the historical scenario is stored alongside other, future, scenarios.
-    \*\*hist : dict
+    **hist : dict
         Mapping of the scenario dimension name to the historical scenario coordinate, e.g. `scenario="historical"`.
 
     Returns
diff --git a/xclim/ensembles/_reduce.py b/xclim/ensembles/_reduce.py
index ebe27e6dd..e73d431f9 100644
--- a/xclim/ensembles/_reduce.py
+++ b/xclim/ensembles/_reduce.py
@@ -150,7 +150,7 @@ def kkz_reduce_ensemble(
     standardize : bool
         Whether to standardize the input before running the selection or not.
         Standardization consists in translation as to have a zero mean and scaling as to have a unit standard deviation.
-    \*\*cdist_kwargs : dict
+    **cdist_kwargs : dict
         All extra arguments are passed as-is to `scipy.spatial.distance.cdist`, see its docs for more information.
 
     Returns
diff --git a/xclim/ensembles/_robustness.py b/xclim/ensembles/_robustness.py
index 677e1e5fe..309e3d1be 100644
--- a/xclim/ensembles/_robustness.py
+++ b/xclim/ensembles/_robustness.py
@@ -89,7 +89,7 @@ def robustness_fractions(  # noqa: C901
         Name of the statistical test used to determine if there was significant change. See notes.
     weights : xr.DataArray
         Weights to apply along the 'realization' dimension. This array cannot contain missing values.
-    \*\*kwargs : dict
+    **kwargs : dict
         Other arguments specific to the statistical test. See notes.
 
     Returns
diff --git a/xclim/indicators/atmos/_conversion.py b/xclim/indicators/atmos/_conversion.py
index a8b86da33..cb0259d13 100644
--- a/xclim/indicators/atmos/_conversion.py
+++ b/xclim/indicators/atmos/_conversion.py
@@ -42,7 +42,7 @@ def cfcheck(self, **das) -> None:
 
         Parameters
         ----------
-        \*\*das : Mapping[str, xarray.DataArray]
+        **das : Mapping[str, xarray.DataArray]
             The input data arrays.
         """
         for varname, vardata in das.items():
diff --git a/xclim/indices/_agro.py b/xclim/indices/_agro.py
index 1b82d9340..fcff347a0 100644
--- a/xclim/indices/_agro.py
+++ b/xclim/indices/_agro.py
@@ -1151,7 +1151,7 @@ def standardized_precipitation_index(
         Fit parameters.
         The `params` can be computed using ``xclim.indices.stats.standardized_index_fit_params`` in advance.
         The output can be given here as input, and it overrides other options.
-    \*\*indexer : {dim: indexer}, optional
+    **indexer : {dim: indexer}, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
 
@@ -1286,7 +1286,7 @@ def standardized_precipitation_evapotranspiration_index(
         Fit parameters.
         The `params` can be computed using ``xclim.indices.stats.standardized_index_fit_params`` in advance.
         The output can be given here as input, and it overrides other options.
-    \*\*indexer : {dim: indexer}, optional
+    **indexer : {dim: indexer}, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
 
@@ -1599,7 +1599,7 @@ def chill_portions(
         Hourly temperature.
     freq : str
         Resampling frequency.
-    \*\*indexer : {dim: indexer}, optional
+    **indexer : {dim: indexer}, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
 
diff --git a/xclim/indices/_conversion.py b/xclim/indices/_conversion.py
index 032e66e4c..39d6c8f63 100644
--- a/xclim/indices/_conversion.py
+++ b/xclim/indices/_conversion.py
@@ -2106,7 +2106,7 @@ def wind_profile(
         Reference height.
     method : {"power_law"}
         Method to use. Currently only "power_law" is implemented.
-    \*\*kwds : dict
+    **kwds : dict
         Additional keyword arguments to pass to the method.For power_law, this is alpha, which takes a default value
         of 1/7, but is highly variable based on topography, surface cover and atmospheric stability.
 
diff --git a/xclim/indices/_simple.py b/xclim/indices/_simple.py
index c13ab9e7e..f9697e9cd 100644
--- a/xclim/indices/_simple.py
+++ b/xclim/indices/_simple.py
@@ -336,7 +336,7 @@ def frost_days(
         Freezing temperature.
     freq : str
         Resampling frequency.
-    \*\*indexer : {dim: indexer}, optional
+    **indexer : {dim: indexer}, optional
         Indexing parameters to compute the frost days on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
 
diff --git a/xclim/indices/_threshold.py b/xclim/indices/_threshold.py
index 25bca2407..6495056ac 100644
--- a/xclim/indices/_threshold.py
+++ b/xclim/indices/_threshold.py
@@ -3193,7 +3193,7 @@ def dry_spell_frequency(
         threshold.
         "max" checks that the maximal daily precipitation amount within the window is less than the threshold.
         This is the same as verifying that each individual day is below the threshold.
-    \*\*indexer : {dim: indexer}, optional
+    **indexer : {dim: indexer}, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
         Indexing is done after finding the dry days, but before finding the spells.
@@ -3261,7 +3261,7 @@ def dry_spell_total_length(
         Resampling frequency.
     resample_before_rl : bool
         Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs.
-    \*\*indexer : {dim: indexer}, optional
+    **indexer : {dim: indexer}, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
         Indexing is done after finding the dry days, but before finding the spells.
@@ -3328,7 +3328,7 @@ def dry_spell_max_length(
     resample_before_rl : bool
         Determines if the resampling should take place before or after the run
         length encoding (or a similar algorithm) is applied to runs.
-    \*\*indexer : {dim: indexer}, optional
+    **indexer : {dim: indexer}, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
         Indexing is done after finding the dry days, but before finding the spells.
@@ -3398,7 +3398,7 @@ def wet_spell_frequency(
         threshold.
         "min" checks that the maximal daily precipitation amount within the window is more than the threshold.
         This is the same as verifying that each individual day is above the threshold.
-    \*\*indexer : {dim: indexer}, optional
+    **indexer : {dim: indexer}, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
         Indexing is done after finding the wet days, but before finding the spells.
@@ -3465,7 +3465,7 @@ def wet_spell_total_length(
         Resampling frequency.
     resample_before_rl : bool
         Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs.
-    \*\*indexer : {dim: indexer}, optional
+    **indexer : {dim: indexer}, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
         Indexing is done after finding the wet days, but before finding the spells.
@@ -3534,7 +3534,7 @@ def wet_spell_max_length(
         Resampling frequency.
     resample_before_rl : bool
         Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs.
-    \*\*indexer : {dim: indexer}, optional
+    **indexer : {dim: indexer}, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
         Indexing is done after finding the wet days, but before finding the spells.
diff --git a/xclim/indices/fire/_cffwis.py b/xclim/indices/fire/_cffwis.py
index 25be1bcda..feffbe97c 100644
--- a/xclim/indices/fire/_cffwis.py
+++ b/xclim/indices/fire/_cffwis.py
@@ -1354,7 +1354,7 @@ def cffwis_indices(
         If True (default), gridpoints where the fire season is active on the first timestep go through a start_up phase
         for that time step. Otherwise, previous codes must be given as a continuing fire season is assumed for those
         points.
-    \*\*params : dict
+    **params : dict
         Any other keyword parameters as defined in :py:func:`fire_weather_ufunc` and in :py:data:`default_params`.
 
     Returns
@@ -1465,7 +1465,7 @@ def drought_code(
         If True (default), grid points where the fire season is active on the first timestep go through a start_up phase
         for that time step. Otherwise, previous codes must be given as a continuing fire season is assumed for those
         points.
-    \*\*params : dict
+    **params : dict
         Any other keyword parameters as defined in `xclim.indices.fire.fire_weather_ufunc` and in :py:data:`default_params`.
 
     Returns
@@ -1560,7 +1560,7 @@ def duff_moisture_code(
         If True (default), grid points where the fire season is active on the first timestep go through a start_up phase
         for that time step. Otherwise, previous codes must be given as a continuing fire season is assumed for those
         points.
-    \*\*params : dict
+    **params : dict
         Any other keyword parameters as defined in `xclim.indices.fire.fire_weather_ufunc` and in :py:data:`default_params`.
 
     Returns
diff --git a/xclim/indices/generic.py b/xclim/indices/generic.py
index c7657a45a..510c6eb45 100644
--- a/xclim/indices/generic.py
+++ b/xclim/indices/generic.py
@@ -83,7 +83,7 @@ def select_resample_op(
         Resampling frequency defining the periods as defined in :ref:`timeseries.resampling`.
     out_units : str, optional
         Output units to assign. Only necessary if `op` is function not supported by :py:func:`xclim.core.units.to_agg_units`.
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Time attribute and values over which to subset the array. For example, use season='DJF' to select winter values,
         month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given, all values are
         considered.
@@ -144,7 +144,7 @@ def select_rolling_resample_op(
         Resampling frequency defining the periods as defined in :ref:`timeseries.resampling`. Applied after the rolling window.
     out_units : str, optional
         Output units to assign. Only necessary if `op` is function not supported by :py:func:`xclim.core.units.to_agg_units`.
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Time attribute and values over which to subset the array. For example, use season='DJF' to select winter values,
         month=1 to select January, or month=[6,7,8] to select summer months. If not indexer is given, all values are
         considered.
@@ -209,7 +209,7 @@ def default_freq(**indexer) -> str:
 
     Parameters
     ----------
-    \*\*indexer : {dim: indexer, }
+    **indexer : {dim: indexer, }
         The indexer to use to compute the frequency.
 
     Returns
@@ -618,7 +618,7 @@ def spell_length_statistics(
     resample_before_rl : bool
         Determines if the resampling should take place before or after the run
         length encoding (or a similar algorithm) is applied to runs.
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
         Indexing is done after finding the days part of a spell, but before taking the spell statistics.
@@ -725,7 +725,7 @@ def bivariate_spell_length_statistics(
     resample_before_rl : bool
         Determines if the resampling should take place before or after the run
         length encoding (or a similar algorithm) is applied to runs.
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
         Indexing is done after finding the days part of a spell, but before taking the spell statistics.
diff --git a/xclim/indices/run_length.py b/xclim/indices/run_length.py
index e5cb3e54d..df1646676 100644
--- a/xclim/indices/run_length.py
+++ b/xclim/indices/run_length.py
@@ -99,13 +99,13 @@ def resample_and_rl(
         or after the run length algorithms are applied.
     compute : Callable
         Run length function to apply.
-    \*args : Any
+    *args : Any
         Positional arguments needed in `compute`.
     freq : str
         Resampling frequency.
     dim : str
         The dimension along which to find runs.
-    \*\*kwargs : dict
+    **kwargs : dict
         Keyword arguments needed in `compute`.
 
     Returns
diff --git a/xclim/indices/stats.py b/xclim/indices/stats.py
index 51250e3b9..a1709ca6d 100644
--- a/xclim/indices/stats.py
+++ b/xclim/indices/stats.py
@@ -98,7 +98,7 @@ def fit(
         The PWM method is usually more robust to outliers.
     dim : str
         The dimension upon which to perform the indexing (default: "time").
-    \*\*fitkwargs : dict
+    **fitkwargs : dict
         Other arguments passed directly to :py:func:`_fitstart` and to the distribution's `fit`.
 
     Returns
@@ -403,7 +403,7 @@ def frequency_analysis(
         Fitting method, either maximum likelihood (ML or MLE), method of moments (MOM) or approximate method (APP).
         Also accepts probability weighted moments (PWM), also called L-Moments, if `dist` is an instance from the lmoments3 library.
         The PWM method is usually more robust to outliers.
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Time attribute and values over which to subset the array. For example, use season='DJF' to select winter values,
         month=1 to select January, or month=[6,7,8] to select summer months.
         If indexer is not provided, all values are considered.
@@ -473,7 +473,7 @@ def _fit_start(x, dist: str, **fitkwargs: Any) -> tuple[tuple, dict]:
         Name of the univariate distribution, e.g. `beta`, `expon`, `genextreme`, `gamma`, `gumbel_r`, `lognorm`, `norm`.
         (see :py:mod:scipy.stats).
         Only `genextreme` and `weibull_exp` distributions are supported.
-    \*\*fitkwargs
+    **fitkwargs
         Kwargs passed to fit.
 
     Returns
@@ -574,13 +574,13 @@ def _dist_method_1D(  # noqa: N802
 
     Parameters
     ----------
-    \*args
+    *args
         The arguments for the requested scipy function.
     dist : str or rv_continuous distribution object
         The scipy name of the distribution.
     function : str
         The name of the function to call.
-    \*\*kwargs : dict
+    **kwargs : dict
         Other parameters to pass to the function call.
 
     Returns
@@ -615,7 +615,7 @@ def dist_method(
         The first argument for the requested function if different from `fit_params`.
     dist : str or rv_continuous distribution object, optional
         The distribution name or instance. Defaults to the `scipy_dist` attribute or `fit_params`.
-    \*\*kwargs : dict
+    **kwargs : dict
         Other parameters to pass to the function call.
 
     Returns
@@ -666,7 +666,7 @@ def preprocess_standardized_index(
     window : int
         Averaging window length relative to the resampling frequency. For example, if `freq="MS"`,
         i.e. a monthly resampling, the window is an integer number of months.
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
 
@@ -754,7 +754,7 @@ def standardized_index_fit_params(
         If True, the zeroes of `da` are treated separately when fitting a probability density function.
     fitkwargs : dict, optional
         Kwargs passed to ``xclim.indices.stats.fit`` used to impose values of certains parameters (`floc`, `fscale`).
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
 
@@ -873,7 +873,7 @@ def standardized_index(
         Fit parameters.
         The `params` can be computed using ``xclim.indices.stats.standardized_index_fit_params`` in advance.
         The output can be given here as input, and it overrides other options.
-    \*\*indexer : {dim: indexer, }, optional
+    **indexer : {dim: indexer, }, optional
         Indexing parameters to compute the indicator on a temporal subset of the data.
         It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
 
diff --git a/xclim/sdba/_adjustment.py b/xclim/sdba/_adjustment.py
index 5800b72bf..238058824 100644
--- a/xclim/sdba/_adjustment.py
+++ b/xclim/sdba/_adjustment.py
@@ -696,7 +696,7 @@ def npdf_transform(ds: xr.Dataset, **kwargs) -> xr.Dataset:
             hist : simulated timeseries on the reference period
             sim : Simulated timeseries on the projected period.
             rot_matrices : Random rotation matrices.
-    \*\*kwargs : dict
+    **kwargs : dict
         pts_dim : multivariate dimension name
         base : Adjustment class
         base_kws : Kwargs for initialising the adjustment object
diff --git a/xclim/sdba/adjustment.py b/xclim/sdba/adjustment.py
index c7611fddb..2f16cc26e 100644
--- a/xclim/sdba/adjustment.py
+++ b/xclim/sdba/adjustment.py
@@ -245,7 +245,7 @@ def train(cls, ref: DataArray, hist: DataArray, **kwargs) -> TrainAdjust:
             Training target, usually a reference time series drawn from observations.
         hist : DataArray
             Training data, usually a model output whose biases are to be adjusted.
-        \*\*kwargs : dict
+        **kwargs : dict
             Algorithm-specific keyword arguments, see class doc.
         """
         kwargs = parse_group(cls._train, kwargs)
@@ -278,9 +278,9 @@ def adjust(self, sim: DataArray, *args: xr.DataArray, **kwargs):
         ----------
         sim : DataArray
             Time series to be bias-adjusted, usually a model output.
-        \*args : xr.DataArray
+        *args : xr.DataArray
             Other DataArrays needed for the adjustment (usually none).
-        \*\*kwargs : dict
+        **kwargs : dict
             Algorithm-specific keyword arguments, see class doc.
         """
         skip_checks = kwargs.pop("skip_input_checks", False)
@@ -358,7 +358,7 @@ def adjust(
             Training data, usually a model output whose biases are to be adjusted.
         sim : DataArray
             Time series to be bias-adjusted, usually a model output.
-        \*\*kwargs : dict
+        **kwargs : dict
             Algorithm-specific keyword arguments, see class doc.
 
         Returns
diff --git a/xclim/sdba/base.py b/xclim/sdba/base.py
index 3e66c33aa..c060a054b 100644
--- a/xclim/sdba/base.py
+++ b/xclim/sdba/base.py
@@ -346,7 +346,7 @@ def apply(
             (if False, default) (including the window and dimensions given through `add_dims`).
             The dimensions used are also written in the "group_compute_dims" attribute.
             If all the input arrays are missing one of the 'add_dims', it is silently omitted.
-        \*\*kwargs : dict
+        **kwargs : dict
             Other keyword arguments to pass to the function.
 
         Returns
@@ -524,7 +524,7 @@ def map_blocks(  # noqa: C901
     ----------
     reduces : sequence of strings
         Name of the dimensions that are removed by the function.
-    \*\*out_vars
+    **out_vars
         Mapping from variable names in the output to their *new* dimensions.
         The placeholders ``Grouper.PROP``, ``Grouper.DIM`` and ``Grouper.ADD_DIMS`` can be used to signify
         ``group.prop``,``group.dim`` and ``group.add_dims`` respectively.
@@ -747,7 +747,7 @@ def map_groups(
         if main_only is False, and [Grouper.DIM] if main_only is True. See :py:func:`map_blocks`.
     main_only : bool
         Same as for :py:meth:`Grouper.apply`.
-    \*\*out_vars
+    **out_vars
         Mapping from variable names in the output to their *new* dimensions.
         The placeholders ``Grouper.PROP``, ``Grouper.DIM`` and ``Grouper.ADD_DIMS`` can be used to signify
         ``group.prop``,``group.dim`` and ``group.add_dims``, respectively.
diff --git a/xclim/testing/utils.py b/xclim/testing/utils.py
index d2ea0da89..83fa276ec 100644
--- a/xclim/testing/utils.py
+++ b/xclim/testing/utils.py
@@ -609,7 +609,7 @@ def open_dataset(
         URL of the repository to use when fetching testing datasets.
     cache_dir : Path
         The directory in which to search for and write cached data.
-    \*\*kwargs : dict
+    **kwargs : dict
         For NetCDF files, keywords passed to :py:func:`xarray.open_dataset`.
 
     Returns