diff --git a/doc/conf.py b/doc/conf.py
index 9bf90ea6b..4f0cec77b 100755
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -119,6 +119,8 @@
     "ml": "pastas.model.Model",
     "TimestampType": "pandas.Timestamp",
 }
+# add custom section to docstrings in Parameters style
+napoleon_custom_sections = [("Time series settings", "params_style")]
 
 # -- Autodoc, autosummary, and autosectionlabel settings ------------------------------
 
diff --git a/pastas/__init__.py b/pastas/__init__.py
index cd272c95b..933d36e6d 100644
--- a/pastas/__init__.py
+++ b/pastas/__init__.py
@@ -3,10 +3,12 @@
 
 from pandas.plotting import register_matplotlib_converters
 
+import pastas.objective_functions as objfunc
 import pastas.plots as plots
 import pastas.recharge as rch
 import pastas.stats as stats
 import pastas.timeseries_utils as ts
+
 from .decorators import set_use_numba
 from .model import Model
 from .modelcompare import CompareModels
@@ -25,8 +27,7 @@
     Polder,
     Spline,
 )
-from .solver import LeastSquares, LmfitSolve, EmceeSolve
-import pastas.objective_functions as objfunc
+from .solver import EmceeSolve, LeastSquares, LmfitSolve
 from .stressmodels import (
     ChangeModel,
     Constant,
diff --git a/pastas/objective_functions.py b/pastas/objective_functions.py
index c0b66a9e9..66bac17bf 100644
--- a/pastas/objective_functions.py
+++ b/pastas/objective_functions.py
@@ -2,7 +2,7 @@
 `EmceeSolve` solver.   
 
 """
-from numpy import pi, log
+from numpy import log, pi
 from pandas import DataFrame
 
 
diff --git a/pastas/rcparams.py b/pastas/rcparams.py
index 85a3115d0..68a8d57ae 100644
--- a/pastas/rcparams.py
+++ b/pastas/rcparams.py
@@ -1,54 +1,59 @@
+from .typing import OseriesSettingsDict, StressSettingsDict
+
 rcParams = {
     "timeseries": {
-        "oseries": {"fill_nan": "drop", "sample_down": "drop"},
-        "prec": {
-            "sample_up": "bfill",
-            "sample_down": "mean",
-            "fill_nan": 0.0,
-            "fill_before": "mean",
-            "fill_after": "mean",
-        },
-        "evap": {
-            "sample_up": "bfill",
-            "sample_down": "mean",
-            "fill_before": "mean",
-            "fill_after": "mean",
-            "fill_nan": "interpolate",
-        },
-        "well": {
-            "sample_up": "bfill",
-            "sample_down": "mean",
-            "fill_nan": 0.0,
-            "fill_before": 0.0,
-            "fill_after": 0.0,
-        },
-        "waterlevel": {
-            "sample_up": "interpolate",
-            "sample_down": "mean",
-            "fill_before": "mean",
-            "fill_after": "mean",
-            "fill_nan": "interpolate",
-        },
-        "level": {
-            "sample_up": "interpolate",
-            "sample_down": "mean",
-            "fill_before": "mean",
-            "fill_after": "mean",
-            "fill_nan": "interpolate",
-        },
-        "flux": {
-            "sample_up": "bfill",
-            "sample_down": "mean",
-            "fill_before": "mean",
-            "fill_after": "mean",
-            "fill_nan": 0.0,
-        },
-        "quantity": {
-            "sample_up": "divide",
-            "sample_down": "sum",
-            "fill_before": "mean",
-            "fill_after": "mean",
-            "fill_nan": 0.0,
-        },
+        "oseries": OseriesSettingsDict(
+            fill_nan="drop",
+            sample_down="drop",
+        ),
+        "prec": StressSettingsDict(
+            sample_up="bfill",
+            sample_down="mean",
+            fill_nan=0.0,
+            fill_before="mean",
+            fill_after="mean",
+        ),
+        "evap": StressSettingsDict(
+            sample_up="bfill",
+            sample_down="mean",
+            fill_before="mean",
+            fill_after="mean",
+            fill_nan="interpolate",
+        ),
+        "well": StressSettingsDict(
+            sample_up="bfill",
+            sample_down="mean",
+            fill_nan=0.0,
+            fill_before=0.0,
+            fill_after=0.0,
+        ),
+        "waterlevel": StressSettingsDict(
+            sample_up="interpolate",
+            sample_down="mean",
+            fill_before="mean",
+            fill_after="mean",
+            fill_nan="interpolate",
+        ),
+        "level": StressSettingsDict(
+            sample_up="interpolate",
+            sample_down="mean",
+            fill_before="mean",
+            fill_after="mean",
+            fill_nan="interpolate",
+        ),
+        "flux": StressSettingsDict(
+            sample_up="bfill",
+            sample_down="mean",
+            fill_before="mean",
+            fill_after="mean",
+            fill_nan=0.0,
+        ),
+        "quantity": StressSettingsDict(
+            sample_up="divide",
+            sample_down="sum",
+            fill_before="mean",
+            fill_after="mean",
+            fill_nan=0.0,
+        ),
     }
 }
diff --git a/pastas/solver.py b/pastas/solver.py
index da7550685..d3a66f1fa 100644
--- a/pastas/solver.py
+++ b/pastas/solver.py
@@ -20,7 +20,6 @@
 from scipy.linalg import svd
 from scipy.optimize import least_squares
 
-
 from pastas.objective_functions import GaussianLikelihood
 from pastas.typing import ArrayLike, CallBack, Function, Model
 
diff --git a/pastas/stressmodels.py b/pastas/stressmodels.py
index 924821bae..544722cf4 100644
--- a/pastas/stressmodels.py
+++ b/pastas/stressmodels.py
@@ -23,7 +23,14 @@
 from pandas import DataFrame, Series, Timedelta, Timestamp, concat, date_range
 from scipy.signal import fftconvolve
 
-from pastas.typing import ArrayLike, Model, Recharge, RFunc, TimestampType
+from pastas.typing import (
+    ArrayLike,
+    Model,
+    Recharge,
+    RFunc,
+    StressSettingsDict,
+    TimestampType,
+)
 
 from .decorators import njit, set_parameter
 from .recharge import Linear
@@ -233,7 +240,6 @@ def get_settings(self) -> dict:
         Notes
         -----
         To update the settings of the time series, use the `update_stress` method.
-
         """
         if len(self.stress) == 0:
             settings = None
@@ -258,9 +264,9 @@ class StressModel(StressModelBase):
         you don't want to define if response is positive or negative.
     settings: dict or str, optional
         The settings of the stress. This can be a string referring to a predefined
-        settings dict (defined in ps.rcParams["timeseries"]), or a dict with the
-        settings to apply. Refer to the docs of pastas.Timeseries for further
-        information.
+        settings dictionary (defined in ps.rcParams["timeseries"]), or a dictionary with
+        the settings to apply. For more information refer to Time series settings
+        section below.
     metadata: dict, optional
         dictionary containing metadata about the stress. This is passed onto the
         TimeSeries object.
@@ -268,6 +274,46 @@ class StressModel(StressModelBase):
         the scale factor is used to set the initial value and the bounds of the gain
         parameter, computed as 1 / gain_scale_factor.
 
+    Time series settings
+    --------------------
+    fill_nan : {"drop", "mean", "interpolate"} or float
+        Method for filling NaNs.
+           * `drop`: drop NaNs from time series
+           * `mean`: fill NaNs with mean value of time series
+           * `interpolate`: fill NaNs by interpolating between finite values
+           * `float`: fill NaN with provided value, e.g. 0.0
+    fill_before : {"mean", "bfill"} or float
+        Method for extending time series into past.
+           * `mean`: extend time series into past with mean value of time series
+           * `bfill`: extend time series into past by back-filling first value
+           * `float`: extend time series into past with provided value, e.g. 0.0
+    fill_after : {"mean", "ffill"} or float
+        Method for extending time series into future.
+           * `mean`: extend time series into future with mean value of time series
+           * `ffill`: extend time series into future by forward-filling last value
+           * `float`: extend time series into future with provided value, e.g. 0.0
+    sample_up : {"mean", "interpolate", "divide"} or float
+        Method for up-sampling time series (increasing frequency, e.g. going from weekly
+        to daily values).
+           * `bfill` or `backfill`: fill up-sampled time steps by back-filling current
+             values
+           * `ffill` or `pad`: fill up-sampled time steps by forward-filling current
+             values
+           * `mean`: fill up-sampled time steps with mean of timeseries
+           * `interpolate`: fill up-sampled time steps by interpolating between current
+             values
+           * `divide`: fill up-sampled steps with current value divided by length of
+             current time steps (i.e. spread value over new time steps).
+    sample_down : {"mean", "drop", "sum", "min", "max"}
+        Method for down-sampling time series (decreasing frequency, e.g. going from
+        daily to weekly values).
+           * `mean`: resample time series by taking the mean
+           * `drop`: resample the time series by taking the mean, dropping any
+             NaN-values
+           * `sum`: resample time series by summing values
+           * `max`: resample time series with maximum value
+           * `min`: resample time series with minimum value
+
     Examples
     --------
     >>> import pastas as ps
@@ -289,7 +335,7 @@ def __init__(
         rfunc: RFunc,
         name: str,
         up: bool = True,
-        settings: Optional[Union[dict, str]] = None,
+        settings: Optional[Union[str, StressSettingsDict]] = None,
         metadata: Optional[dict] = None,
         gain_scale_factor: Optional[float] = None,
     ) -> None:
@@ -365,7 +411,6 @@ def to_dict(self, series: bool = True) -> dict:
         Notes
         -----
         Settings and metadata are exported with the stress.
-
         """
         data = {
             "class": self._name,
@@ -469,7 +514,6 @@ def to_dict(self, **kwargs) -> dict:
         -------
         data: dict
             dictionary with all necessary information to reconstruct object.
-
         """
         data = {
             "class": self._name,
@@ -583,7 +627,6 @@ def to_dict(self, **kwargs) -> dict:
         Returns
         -------
         data: dict
-
         """
         data = {
             "class": self._name,
@@ -669,12 +712,52 @@ class WellModel(StressModelBase):
         level.
     settings: str, list of dict, optional
         The settings of the stress. By default this is "well". This can be a string
-        referring to a predefined settings dict (defined in
-        ps.rcParams["timeseries"]), or a dict with the settings to apply. Refer
-        to the docs of pastas.Timeseries for further information.
+        referring to a predefined settings dictionary (defined in
+        ps.rcParams["timeseries"]), or a dictionary with the settings to apply. For more
+        information, refer to Time series settings section below.
     sort_wells: bool, optional
         sort wells from closest to furthest, by default True.
 
+    Time series settings
+    --------------------
+    fill_nan : {"drop", "mean", "interpolate"} or float
+        Method for filling NaNs.
+           * `drop`: drop NaNs from time series
+           * `mean`: fill NaNs with mean value of time series
+           * `interpolate`: fill NaNs by interpolating between finite values
+           * `float`: fill NaN with provided value, e.g. 0.0
+    fill_before : {"mean", "bfill"} or float
+        Method for extending time series into past.
+           * `mean`: extend time series into past with mean value of time series
+           * `bfill`: extend time series into past by back-filling first value
+           * `float`: extend time series into past with provided value, e.g. 0.0
+    fill_after : {"mean", "ffill"} or float
+        Method for extending time series into future.
+           * `mean`: extend time series into future with mean value of time series
+           * `ffill`: extend time series into future by forward-filling last value
+           * `float`: extend time series into future with provided value, e.g. 0.0
+    sample_up : {"mean", "interpolate", "divide"} or float
+        Method for up-sampling time series (increasing frequency, e.g. going from weekly
+        to daily values).
+           * `bfill` or `backfill`: fill up-sampled time steps by back-filling current
+             values
+           * `ffill` or `pad`: fill up-sampled time steps by forward-filling current
+             values
+           * `mean`: fill up-sampled time steps with mean of timeseries
+           * `interpolate`: fill up-sampled time steps by interpolating between current
+             values
+           * `divide`: fill up-sampled steps with current value divided by length of
+             current time steps (i.e. spread value over new time steps).
+    sample_down : {"mean", "drop", "sum", "min", "max"}
+        Method for down-sampling time series (decreasing frequency, e.g. going from
+        daily to weekly values).
+           * `mean`: resample time series by taking the mean
+           * `drop`: resample the time series by taking the mean, dropping any
+             NaN-values
+           * `sum`: resample time series by summing values
+           * `max`: resample time series with maximum value
+           * `min`: resample time series with minimum value
+
     Notes
     -----
     This class implements convolution of multiple series with the same response
@@ -694,7 +777,7 @@ def __init__(
         distances: ArrayLike,
         rfunc: Optional[RFunc] = None,
         up: bool = False,
-        settings: str = "well",
+        settings: Union[str, StressSettingsDict] = "well",
         sort_wells: bool = True,
         metadata: Optional[list] = None,
     ) -> None:
@@ -901,7 +984,6 @@ def get_parameters(self, model=None, istress: Optional[int] = None) -> ArrayLike
         p : array_like
             parameters for each stress as row of array, if istress is used returns
             only one row.
-
         """
         if model is None:
             p = self.parameters.initial.values
@@ -1039,35 +1121,70 @@ class RechargeModel(StressModelBase):
         It depends on the recharge model if this argument is required or not. The
         temperature series should be provided in degrees Celsius.
     settings: list of dicts or str, optional
-        The settings of the precipitation and evaporation time series, in this order.
-        This can be a string referring to a predefined settings dict (defined in
-        ps.rcParams["timeseries"]), or a dict with the settings to apply. Refer to
-        the docs of pastas.Timeseries for further information.
+        The settings of the precipitation, evaporation and optionally temperature time
+        series, in this order. By default ("prec", "evap", "evap"). This can be a string
+        referring to a predefined settings dict (defined in ps.rcParams["timeseries"]),
+        or a dict with the settings to apply. For more information refer to Time Series
+        Settings section below for more information.
     metadata: tuple of dicts or list of dicts, optional
         dictionary containing metadata about the stress. This is passed onto the
         TimeSeries object.
 
-    See Also
+    Examples
     --------
-    pastas.rfunc
-    pastas.timeseries.TimeSeries
-    pastas.recharge
+    >>> sm = ps.RechargeModel(rain, evap, rfunc=ps.Exponential(),
+    >>>                       recharge=ps.rch.FlexModel(), name="rch")
+    >>> ml.add_stressmodel(sm)
+
+    Time series settings
+    --------------------
+    fill_nan : {"drop", "mean", "interpolate"} or float
+        Method for filling NaNs.
+           * `drop`: drop NaNs from time series
+           * `mean`: fill NaNs with mean value of time series
+           * `interpolate`: fill NaNs by interpolating between finite values
+           * `float`: fill NaN with provided value, e.g. 0.0
+    fill_before : {"mean", "bfill"} or float
+        Method for extending time series into past.
+           * `mean`: extend time series into past with mean value of time series
+           * `bfill`: extend time series into past by back-filling first value
+           * `float`: extend time series into past with provided value, e.g. 0.0
+    fill_after : {"mean", "ffill"} or float
+        Method for extending time series into future.
+           * `mean`: extend time series into future with mean value of time series
+           * `ffill`: extend time series into future by forward-filling last value
+           * `float`: extend time series into future with provided value, e.g. 0.0
+    sample_up : {"mean", "interpolate", "divide"} or float
+        Method for up-sampling time series (increasing frequency, e.g. going from weekly
+        to daily values).
+           * `bfill` or `backfill`: fill up-sampled time steps by back-filling current
+             values
+           * `ffill` or `pad`: fill up-sampled time steps by forward-filling current
+             values
+           * `mean`: fill up-sampled time steps with mean of timeseries
+           * `interpolate`: fill up-sampled time steps by interpolating between current
+             values
+           * `divide`: fill up-sampled steps with current value divided by length of
+             current time steps (i.e. spread value over new time steps).
+    sample_down : {"mean", "drop", "sum", "min", "max"}
+        Method for down-sampling time series (decreasing frequency, e.g. going from
+        daily to weekly values).
+           * `mean`: resample time series by taking the mean
+           * `drop`: resample the time series by taking the mean, dropping any
+             NaN-values
+           * `sum`: resample time series by summing values
+           * `max`: resample time series with maximum value
+           * `min`: resample time series with minimum value
 
     Notes
     -----
     This stress model computes the contribution of precipitation and potential
     evaporation in two steps. In the first step a recharge flux is computed by a
     model determined by the input argument `recharge`. In the second step this
-    recharge flux is convoluted with a response function to obtain the contribution
+    recharge flux is convolved with a response function to obtain the contribution
     of recharge to the groundwater levels. If a nonlinear recharge model is used, the
     precipitation should be in mm/d.
 
-    Examples
-    --------
-    >>> sm = ps.RechargeModel(rain, evap, rfunc=ps.Exponential(),
-    >>>                       recharge=ps.rch.FlexModel(), name="rch")
-    >>> ml.add_stressmodel(sm)
-
     Warnings
     --------
     We recommend not to store a RechargeModel is a variable named `rm`. This name is
@@ -1080,6 +1197,11 @@ class RechargeModel(StressModelBase):
     the precipitation series are in m/d instead of mm/d. Please check the units of the
     precipitation series.
 
+    See Also
+    --------
+    pastas.rfunc
+    pastas.timeseries.TimeSeries
+    pastas.recharge
     """
 
     _name = "RechargeModel"
@@ -1092,7 +1214,11 @@ def __init__(
         name: str = "recharge",
         recharge: Optional[Recharge] = None,
         temp: Optional[Series] = None,
-        settings: Tuple[Union[str, dict], Union[str, dict], Union[str, dict]] = (
+        settings: Tuple[
+            Union[str, StressSettingsDict],
+            Union[str, StressSettingsDict],
+            Union[str, StressSettingsDict],
+        ] = (
             "prec",
             "evap",
             "evap",
@@ -1403,7 +1529,6 @@ def to_dict(self, series: bool = True) -> dict:
         Notes
         -----
         Settings and metadata are exported with the stress.
-
         """
         data = {
             "class": self._name,
@@ -1553,7 +1678,6 @@ def to_dict(self, series: bool = True) -> dict:
         Notes
         -----
         Settings and metadata are exported with the stress.
-
         """
         data = super().to_dict(series)
         data["dmin"] = self.dmin
@@ -1562,8 +1686,8 @@ def to_dict(self, series: bool = True) -> dict:
 
     @staticmethod
     def _check_stressmodel_compatibility(ml: Model) -> None:
-        """Internal method to check if no other stressmodels, a constants or a
-        transform is used."""
+        """Internal method to check if no other stressmodels, a constants or a transform
+        is used."""
         msg = (
             "A TarsoModel cannot be combined with %s. Either remove the TarsoModel or "
             "the %s."
@@ -1578,8 +1702,8 @@ def _check_stressmodel_compatibility(ml: Model) -> None:
     @staticmethod
     @njit
     def tarso(p: ArrayLike, r: ArrayLike, dt: float) -> ArrayLike:
-        """Calculates the head based on exponential decay of the previous timestep
-        and recharge, using two thresholds."""
+        """Calculates the head based on exponential decay of the previous timestep and
+        recharge, using two thresholds."""
         A0, a0, d0, A1, a1, d1 = p
 
         # calculate physical meaning of these parameters
@@ -1646,12 +1770,52 @@ class ChangeModel(StressModelBase):
     settings: dict or str, optional
         The settings of the stress. This can be a string referring to a predefined
         settings dict (defined in ps.rcParams["timeseries"]), or a dict with the
-        settings to apply. Refer to the docs of pastas.Timeseries for further
-        information.
+        settings to apply. For more information, refer to the docs of pastas.Timeseries
+        for further information.
     metadata: dict, optional
         dictionary containing metadata about the stress. This is passed onto the
         TimeSeries object.
 
+    Time series settings
+    --------------------
+    fill_nan : {"drop", "mean", "interpolate"} or float
+        Method for filling NaNs.
+           * `drop`: drop NaNs from time series
+           * `mean`: fill NaNs with mean value of time series
+           * `interpolate`: fill NaNs by interpolating between finite values
+           * `float`: fill NaN with provided value, e.g. 0.0
+    fill_before : {"mean", "bfill"} or float
+        Method for extending time series into past.
+           * `mean`: extend time series into past with mean value of time series
+           * `bfill`: extend time series into past by back-filling first value
+           * `float`: extend time series into past with provided value, e.g. 0.0
+    fill_after : {"mean", "ffill"} or float
+        Method for extending time series into future.
+           * `mean`: extend time series into future with mean value of time series
+           * `ffill`: extend time series into future by forward-filling last value
+           * `float`: extend time series into future with provided value, e.g. 0.0
+    sample_up : {"mean", "interpolate", "divide"} or float
+        Method for up-sampling time series (increasing frequency, e.g. going from weekly
+        to daily values).
+           * `bfill` or `backfill`: fill up-sampled time steps by back-filling current
+             values
+           * `ffill` or `pad`: fill up-sampled time steps by forward-filling current
+             values
+           * `mean`: fill up-sampled time steps with mean of timeseries
+           * `interpolate`: fill up-sampled time steps by interpolating between current
+             values
+           * `divide`: fill up-sampled steps with current value divided by length of
+             current time steps (i.e. spread value over new time steps).
+    sample_down : {"mean", "drop", "sum", "min", "max"}
+        Method for down-sampling time series (decreasing frequency, e.g. going from
+        daily to weekly values).
+           * `mean`: resample time series by taking the mean
+           * `drop`: resample the time series by taking the mean, dropping any
+             NaN-values
+           * `sum`: resample time series by summing values
+           * `max`: resample time series with maximum value
+           * `min`: resample time series with minimum value
+
     Notes
     -----
     This model is based on :cite:t:`obergfell_identification_2019`.
@@ -1667,7 +1831,7 @@ def __init__(
         name: str,
         tchange: Union[str, TimestampType],
         up: bool = True,
-        settings: Optional[Union[dict, str]] = None,
+        settings: Optional[Union[str, StressSettingsDict]] = None,
         metadata: Optional[dict] = None,
     ) -> None:
         stress = TimeSeries(stress, settings=settings, metadata=metadata)
@@ -1771,7 +1935,6 @@ def to_dict(self, series: bool = True):
         Notes
         -----
         Settings and metadata are exported with the stress.
-
         """
         data = {
             "stress": self.stress[0].to_dict(series=series),
diff --git a/pastas/timeseries.py b/pastas/timeseries.py
index 92b37d026..08fd87193 100644
--- a/pastas/timeseries.py
+++ b/pastas/timeseries.py
@@ -25,9 +25,10 @@ class TimeSeries:
         String with the name of the time series, if None is provided, pastas will try
         to derive the name from the series.
     settings: str or dict, optional
-        String with the name of one of the predefined settings (obtained through
-        ps.TimeSeries._predefined_settings.) or a dictionary with the settings to be
-        applied. This does not have to include all the settings arguments.
+        The settings of the stress. This can be a string referring to a predefined
+        settings dictionary (defined in ps.rcParams["timeseries"]), or a dictionary with
+        the settings to apply. For more information refer to Time series settings
+        section below.
     metadata: dict, optional
         Dictionary with metadata of the time series.
 
@@ -36,6 +37,46 @@ class TimeSeries:
     series: pastas.TimeSeries
         Returns a pastas.TimeSeries object.
 
+    Time series settings
+    --------------------
+    fill_nan : {"drop", "mean", "interpolate"} or float
+        Method for filling NaNs.
+           * `drop`: drop NaNs from time series
+           * `mean`: fill NaNs with mean value of time series
+           * `interpolate`: fill NaNs by interpolating between finite values
+           * `float`: fill NaN with provided value, e.g. 0.0
+    fill_before : {"mean", "bfill"} or float
+        Method for extending time series into past.
+           * `mean`: extend time series into past with mean value of time series
+           * `bfill`: extend time series into past by back-filling first value
+           * `float`: extend time series into past with provided value, e.g. 0.0
+    fill_after : {"mean", "ffill"} or float
+        Method for extending time series into future.
+           * `mean`: extend time series into future with mean value of time series
+           * `ffill`: extend time series into future by forward-filling last value
+           * `float`: extend time series into future with provided value, e.g. 0.0
+    sample_up : {"mean", "interpolate", "divide"} or float
+        Method for up-sampling time series (increasing frequency, e.g. going from weekly
+        to daily values).
+           * `bfill` or `backfill`: fill up-sampled time steps by back-filling current
+             values
+           * `ffill` or `pad`: fill up-sampled time steps by forward-filling current
+             values
+           * `mean`: fill up-sampled time steps with mean of timeseries
+           * `interpolate`: fill up-sampled time steps by interpolating between current
+             values
+           * `divide`: fill up-sampled steps with current value divided by length of
+             current time steps (i.e. spread value over new time steps).
+    sample_down : {"mean", "drop", "sum", "min", "max"}
+        Method for down-sampling time series (decreasing frequency, e.g. going from
+        daily to weekly values).
+           * `mean`: resample time series by taking the mean
+           * `drop`: resample the time series by taking the mean, dropping any
+             NaN-values
+           * `sum`: resample time series by summing values
+           * `max`: resample time series with maximum value
+           * `min`: resample time series with minimum value
+
     Examples
     --------
     To obtain the predefined TimeSeries settings, you can run the following line of
@@ -597,7 +638,6 @@ def validate_stress(series: Series):
     --------
 
     >>> ps.validate_stress(series)
-
     """
     return _validate_series(series, equidistant=True)
 
@@ -635,7 +675,6 @@ def validate_oseries(series: Series):
     --------
 
     >>> ps.validate_oseries(series)
-
     """
     return _validate_series(series, equidistant=False)
 
@@ -659,7 +698,6 @@ def _validate_series(series: Series, equidistant: bool = True):
     -----
     If any of these checks are not passed the method will throw an error that needs
     to be fixed by the user.
-
     """
     # Because we are friendly and allow 1D DataFrames
     if isinstance(series, pd.DataFrame):
diff --git a/pastas/typing/__init__.py b/pastas/typing/__init__.py
index 2eb85b6a2..24665819d 100644
--- a/pastas/typing/__init__.py
+++ b/pastas/typing/__init__.py
@@ -7,10 +7,12 @@
     Function,
     Model,
     NoiseModel,
+    OseriesSettingsDict,
     Recharge,
     RFunc,
     Solver,
     StressModel,
+    StressSettingsDict,
     TimeSeries,
     TimestampType,
 )
diff --git a/pastas/typing/types.py b/pastas/typing/types.py
index f84168fda..fb3810b74 100644
--- a/pastas/typing/types.py
+++ b/pastas/typing/types.py
@@ -1,7 +1,7 @@
 # flake8: noqa
 # Type hinting for Pastas library
 # Typing
-from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union
+from typing import TYPE_CHECKING, Any, Callable, TypedDict, TypeVar, Union
 
 # External libraries
 # Matplotlib
@@ -36,3 +36,83 @@
 CallBack = TypeVar("CallBack", bound=Any)  # Callback
 Function = Callable[..., Any]  # Function (e.g. Objective Function)
 RFunc = TypeVar("RFunc", bound="ps.rfunc.RfuncBase")  # rFunc Base
+
+
+class OseriesSettingsDict(TypedDict):
+    """
+    Time series settings is a dictionary defining logic for filling and up- or
+    downsampling time series.
+
+    Time series settings
+    --------------------
+    fill_nan : {"drop", "mean", "interpolate"} or float
+        Method for filling NaNs.
+           * `drop`: drop NaNs from time series
+           * `mean`: fill NaNs with mean value of time series
+           * `interpolate`: fill NaNs by interpolating between finite values
+           * `float`: fill NaN with provided value, e.g. 0.0
+    sample_down : {"drop", "mean", "sum", "min", "max"}
+        Method for down-sampling time series (decreasing frequency, e.g. going from
+        daily to weekly values).
+           * `drop`: resample the time series by taking the mean, dropping any NaN-values
+           * `mean`: resample time series by taking the mean
+           * `sum`: resample time series by summing values
+           * `max`: resample time series with maximum value
+           * `min`: resample time series with minimum value
+    """
+
+    sample_down: str
+    fill_nan: str
+
+
+class StressSettingsDict(TypedDict):
+    """
+    Time series settings is a dictionary defining logic for filling and up- or
+    downsampling time series.
+
+    Time series settings
+    --------------------
+    fill_nan : {"drop", "mean", "interpolate"} or float
+        Method for filling NaNs.
+           * `drop`: drop NaNs from time series
+           * `mean`: fill NaNs with mean value of time series
+           * `interpolate`: fill NaNs by interpolating between finite values
+           * `float`: fill NaN with provided value, e.g. 0.0
+    fill_before : {"mean", "bfill"} or float
+        Method for extending time series into past.
+           * `mean`: extend time series into past with mean value of time series
+           * `bfill`: extend time series into past by back-filling first value
+           * `float`: extend time series into past with provided value, e.g. 0.0
+    fill_after : {"mean", "ffill"} or float
+        Method for extending time series into future.
+           * `mean`: extend time series into future with mean value of time series
+           * `ffill`: extend time series into future by forward-filling last value
+           * `float`: extend time series into future with provided value, e.g. 0.0
+    sample_up : {"mean", "interpolate", "divide"} or float
+        Method for up-sampling time series (increasing frequency, e.g. going from weekly
+        to daily values).
+           * `bfill` or `backfill`: fill up-sampled time steps by back-filling current
+             values
+           * `ffill` or `pad`: fill up-sampled time steps by forward-filling current
+             values
+           * `mean`: fill up-sampled time steps with mean of timeseries
+           * `interpolate`: fill up-sampled time steps by interpolating between current
+             values
+           * `divide`: fill up-sampled steps with current value divided by length of
+             current time steps (i.e. spread value over new time steps).
+    sample_down : {"mean", "drop", "sum", "min", "max"}
+        Method for down-sampling time series (decreasing frequency, e.g. going from
+        daily to weekly values).
+           * `mean`: resample time series by taking the mean
+           * `drop`: resample the time series by taking the mean, dropping any
+             NaN-values
+           * `sum`: resample time series by summing values
+           * `max`: resample time series with maximum value
+           * `min`: resample time series with minimum value
+    """
+
+    sample_up: str
+    sample_down: str
+    fill_nan: Union[str, float]
+    fill_before: Union[str, float]
+    fill_after: Union[str, float]
diff --git a/pyproject.toml b/pyproject.toml
index d1585baf4..7bea1eee2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -119,7 +119,7 @@ legacy_tox_ini = """
     extras = formatting
     commands =
             black pastas --check --diff
-            isort pastas --diff --profile=black
+            isort pastas --check --diff --profile=black
 
     [testenv:lint]
     description = run linters