diff --git a/docs/sphinx/source/reference/iotools.rst b/docs/sphinx/source/reference/iotools.rst
index 481f46ddb5..183f7b7bae 100644
--- a/docs/sphinx/source/reference/iotools.rst
+++ b/docs/sphinx/source/reference/iotools.rst
@@ -36,6 +36,11 @@ of sources and file formats relevant to solar energy modeling.
    iotools.get_cams
    iotools.read_cams
    iotools.parse_cams
+   iotools.get_acis_prism
+   iotools.get_acis_nrcc
+   iotools.get_acis_mpe
+   iotools.get_acis_station_data
+   iotools.get_acis_available_stations
 
 A :py:class:`~pvlib.location.Location` object may be created from metadata
 in some files.
diff --git a/docs/sphinx/source/whatsnew/v0.9.6.rst b/docs/sphinx/source/whatsnew/v0.9.6.rst
index 729de3e457..6adbc3b8a2 100644
--- a/docs/sphinx/source/whatsnew/v0.9.6.rst
+++ b/docs/sphinx/source/whatsnew/v0.9.6.rst
@@ -48,6 +48,11 @@ Enhancements
 * :py:func:`pvlib.iotools.get_psm3` now uses the new NSRDB 3.2.2 endpoint for
   hourly and half-hourly single-year datasets. (:issue:`1591`, :pull:`1736`)
 * The default solar position algorithm (NREL SPA) is now 50-100% faster. (:pull:`1748`)
+* Added functions to retrieve daily precipitation, temperature, and snowfall data
+  from the NOAA's ACIS service: :py:func:`~pvlib.iotools.get_acis_prism`,
+  :py:func:`~pvlib.iotools.get_acis_nrcc`, :py:func:`~pvlib.iotools.get_acis_mpe`,
+  :py:func:`~pvlib.iotools.get_acis_station_data`, and
+  :py:func:`~pvlib.iotools.get_acis_available_stations`. (:issue:`1293`, :pull:`1767`)
 
 Bug fixes
 ~~~~~~~~~
diff --git a/pvlib/iotools/__init__.py b/pvlib/iotools/__init__.py
index 0a94e79f53..cfb49e1414 100644
--- a/pvlib/iotools/__init__.py
+++ b/pvlib/iotools/__init__.py
@@ -20,3 +20,8 @@
 from pvlib.iotools.sodapro import get_cams  # noqa: F401
 from pvlib.iotools.sodapro import read_cams  # noqa: F401
 from pvlib.iotools.sodapro import parse_cams  # noqa: F401
+from pvlib.iotools.acis import get_acis_prism  # noqa: F401
+from pvlib.iotools.acis import get_acis_nrcc  # noqa: F401
+from pvlib.iotools.acis import get_acis_mpe  # noqa: F401
+from pvlib.iotools.acis import get_acis_station_data  # noqa: F401
+from pvlib.iotools.acis import get_acis_available_stations  # noqa: F401
diff --git a/pvlib/iotools/acis.py b/pvlib/iotools/acis.py
new file mode 100644
index 0000000000..3be16cfa4c
--- /dev/null
+++ b/pvlib/iotools/acis.py
@@ -0,0 +1,516 @@
+import requests
+import pandas as pd
+import numpy as np
+
+
+VARIABLE_MAP = {
+    # time series names
+    'pcpn': 'precipitation',
+    'maxt': 'temp_air_max',
+    'avgt': 'temp_air_average',
+    'obst': 'temp_air_observation',
+    'mint': 'temp_air_min',
+    'cdd': 'cooling_degree_days',
+    'hdd': 'heating_degree_days',
+    'gdd': 'growing_degree_days',
+    'snow': 'snowfall',
+    'snwd': 'snowdepth',
+
+    # metadata names
+    'lat': 'latitude',
+    'lon': 'longitude',
+    'elev': 'altitude',
+}
+
+
+def _get_acis(start, end, params, map_variables, url, **kwargs):
+    """
+    generic helper for the public get_acis_X functions
+    """
+    params = {
+        # use pd.to_datetime so that strings (e.g. '2021-01-01') are accepted
+        'sdate': pd.to_datetime(start).strftime('%Y-%m-%d'),
+        'edate': pd.to_datetime(end).strftime('%Y-%m-%d'),
+        'output': 'json',
+        **params,  # endpoint-specific parameters
+    }
+    response = requests.post(url,
+                             json=params,
+                             headers={"Content-Type": "application/json"},
+                             **kwargs)
+    response.raise_for_status()
+    payload = response.json()
+
+    # somewhat inconveniently, the ACIS API tends to return errors as "valid"
+    # responses instead of using proper HTTP error codes:
+    if "error" in payload:
+        raise requests.HTTPError(payload['error'], response=response)
+
+    columns = ['date'] + [e['name'] for e in params['elems']]
+    df = pd.DataFrame(payload['data'], columns=columns)
+    df = df.set_index('date')
+    df.index = pd.to_datetime(df.index)
+    df.index.name = None
+
+    metadata = payload['meta']
+
+    try:
+        # for StnData endpoint, unpack combination "ll" into lat, lon 
+        metadata['lon'], metadata['lat'] = metadata.pop('ll')
+    except KeyError:
+        pass
+
+    try:
+        metadata['elev'] = metadata['elev'] * 0.3048  # feet to meters
+    except KeyError:
+        # some queries don't return elevation
+        pass
+
+    if map_variables:
+        df = df.rename(columns=VARIABLE_MAP)
+
+        for key in list(metadata.keys()):
+            if key in VARIABLE_MAP:
+                metadata[VARIABLE_MAP[key]] = metadata.pop(key)
+
+    return df, metadata
+
+
+def get_acis_prism(latitude, longitude, start, end, map_variables=True,
+                   url="https://data.rcc-acis.org/GridData", **kwargs):
+    """
+    Retrieve estimated daily precipitation and temperature data from PRISM
+    via the Applied Climate Information System (ACIS).
+
+    ACIS [2]_, [3]_ aggregates and provides access to climate data
+    from many underlying sources.  This function retrieves daily data from
+    the Parameter-elevation Regressions on Independent Slopes Model
+    (PRISM) [1]_, a gridded precipitation and temperature model
+    from Oregon State University.
+
+    Geographical coverage: US, Central America, and part of South America.
+    Approximately 0° to 50° in latitude and -130° to -65° in longitude.
+
+    Parameters
+    ----------
+    latitude : float
+        in decimal degrees, between -90 and 90, north is positive
+    longitude : float
+        in decimal degrees, between -180 and 180, east is positive
+    start : datetime-like
+        First day of the requested period
+    end : datetime-like
+        Last day of the requested period
+    map_variables : bool, default True
+        When True, rename data columns and metadata keys to pvlib variable
+        names where applicable. See variable :const:`VARIABLE_MAP`.
+    url : str, default: 'https://data.rcc-acis.org/GridData'
+        API endpoint URL
+    kwargs:
+        Optional parameters passed to ``requests.post``.
+
+    Returns
+    -------
+    data : pandas.DataFrame
+        Daily precipitation [mm], temperature [Celsius], and degree day
+        [Celsius-days] data
+    metadata : dict
+        Metadata of the selected grid cell
+
+    Raises
+    ------
+    requests.HTTPError
+        A message from the ACIS server if the request is rejected
+
+    Notes
+    -----
+    PRISM data is aggregated from 12:00 to 12:00 UTC, meaning data labeled
+    May 26 reflects to the 24 hours ending at 7:00am Eastern Standard Time
+    on May 26.
+
+    References
+    ----------
+    .. [1] `PRISM <https://prism.oregonstate.edu/>`_
+    .. [2] `ACIS Gridded Data <http://www.rcc-acis.org/docs_gridded.html>`_
+    .. [3] `ACIS Web Services <http://www.rcc-acis.org/docs_webservices.html>`_
+
+    Examples
+    --------
+    >>> from pvlib.iotools import get_acis_prism
+    >>> df, meta = get_acis_prism(40, 80, '2020-01-01', '2020-12-31')
+    """
+    elems = [
+        {"name": "pcpn", "interval": "dly", "units": "mm"},
+        {"name": "maxt", "interval": "dly", "units": "degreeC"},
+        {"name": "mint", "interval": "dly", "units": "degreeC"},
+        {"name": "avgt", "interval": "dly", "units": "degreeC"},
+        {"name": "cdd", "interval": "dly", "units": "degreeC"},
+        {"name": "hdd", "interval": "dly", "units": "degreeC"},
+        {"name": "gdd", "interval": "dly", "units": "degreeC"},
+    ]
+    params = {
+        'loc': f"{longitude},{latitude}",
+        'grid': "21",
+        'elems': elems,
+        'meta': ["ll", "elev"],
+    }
+    df, meta = _get_acis(start, end, params, map_variables, url, **kwargs)
+    df = df.replace(-999, np.nan)
+    return df, meta
+
+
+def get_acis_nrcc(latitude, longitude, start, end, grid, map_variables=True,
+                  url="https://data.rcc-acis.org/GridData", **kwargs):
+    """
+    Retrieve estimated daily precipitation and temperature data from the
+    Northeast Regional Climate Center via the Applied Climate
+    Information System (ACIS).
+
+    ACIS [2]_, [3]_ aggregates and provides access to climate data
+    from many underlying sources.  This function retrieves daily data from
+    Cornell's Northeast Regional Climate Center (NRCC) [1]_.
+
+    Geographical coverage: US, Central America, and part of South America.
+    Approximately 0° to 50° in latitude and -130° to -65° in longitude.
+
+    Parameters
+    ----------
+    latitude : float
+        in decimal degrees, between -90 and 90, north is positive
+    longitude : float
+        in decimal degrees, between -180 and 180, east is positive
+    start : datetime-like
+        First day of the requested period
+    end : datetime-like
+        Last day of the requested period
+    grid : int
+        Options are either 1 (for "NRCC Interpolated") or 3
+        (for "NRCC Hi-Resolution").  See [2]_ for details.
+    map_variables : bool, default True
+        When True, rename data columns and metadata keys to pvlib variable
+        names where applicable. See variable :const:`VARIABLE_MAP`.
+    url : str, default: 'https://data.rcc-acis.org/GridData'
+        API endpoint URL
+    kwargs:
+        Optional parameters passed to ``requests.post``.
+
+    Returns
+    -------
+    data : pandas.DataFrame
+        Daily precipitation [mm], temperature [Celsius], and degree day
+        [Celsius-days] data
+    metadata : dict
+        Metadata of the selected grid cell
+
+    Raises
+    ------
+    requests.HTTPError
+        A message from the ACIS server if the request is rejected
+
+    Notes
+    -----
+    The returned values are 24-hour aggregates, but
+    the aggregation period may not be midnight to midnight in local time.
+    Check the ACIS and NRCC documentation for details.
+
+    References
+    ----------
+    .. [1] `NRCC <http://www.nrcc.cornell.edu/>`_
+    .. [2] `ACIS Gridded Data <http://www.rcc-acis.org/docs_gridded.html>`_
+    .. [3] `ACIS Web Services <http://www.rcc-acis.org/docs_webservices.html>`_
+
+    Examples
+    --------
+    >>> from pvlib.iotools import get_acis_nrcc
+    >>> df, meta = get_acis_nrcc(40, -80, '2020-01-01', '2020-12-31', grid=1)
+    """
+    elems = [
+        {"name": "pcpn", "interval": "dly", "units": "mm"},
+        {"name": "maxt", "interval": "dly", "units": "degreeC"},
+        {"name": "mint", "interval": "dly", "units": "degreeC"},
+        {"name": "avgt", "interval": "dly", "units": "degreeC"},
+        {"name": "cdd", "interval": "dly", "units": "degreeC"},
+        {"name": "hdd", "interval": "dly", "units": "degreeC"},
+        {"name": "gdd", "interval": "dly", "units": "degreeC"},
+    ]
+    params = {
+        'loc': f"{longitude},{latitude}",
+        'grid': grid,
+        'elems': elems,
+        'meta': ["ll", "elev"],
+    }
+    df, meta = _get_acis(start, end, params, map_variables, url, **kwargs)
+    df = df.replace(-999, np.nan)
+    return df, meta
+
+
+
+def get_acis_mpe(latitude, longitude, start, end, map_variables=True,
+                 url="https://data.rcc-acis.org/GridData", **kwargs):
+    """
+    Retrieve estimated daily Multi-sensor Precipitation Estimates
+    via the Applied Climate Information System (ACIS).
+
+    ACIS [2]_, [3]_ aggregates and provides access to climate data
+    from many underlying sources.  This function retrieves daily data from
+    the National Weather Service's Multi-sensor Precipitation Estimates
+    (MPE) [1]_, a gridded precipitation model.
+
+    This dataset covers the contiguous United States, Mexico, and parts of
+    Central America.
+
+    Parameters
+    ----------
+    latitude : float
+        in decimal degrees, between -90 and 90, north is positive
+    longitude : float
+        in decimal degrees, between -180 and 180, east is positive
+    start : datetime-like
+        First day of the requested period
+    end : datetime-like
+        Last day of the requested period
+    map_variables : bool, default True
+        When True, rename data columns and metadata keys to pvlib variable
+        names where applicable. See variable :const:`VARIABLE_MAP`.
+    url : str, default: 'https://data.rcc-acis.org/GridData'
+        API endpoint URL
+    kwargs:
+        Optional parameters passed to ``requests.post``.
+
+    Returns
+    -------
+    data : pandas.DataFrame
+        Daily precipitation [mm] data
+    metadata : dict
+        Coordinates of the selected grid cell
+
+    Raises
+    ------
+    requests.HTTPError
+        A message from the ACIS server if the request is rejected
+
+    Notes
+    -----
+    The returned values are 24-hour aggregates, but
+    the aggregation period may not be midnight to midnight in local time.
+    Check the ACIS and MPE documentation for details.
+
+    References
+    ----------
+    .. [1] `Multisensor Precipitation Estimates
+           <https://www.weather.gov/marfc/Multisensor_Precipitation>`_
+    .. [2] `ACIS Gridded Data <http://www.rcc-acis.org/docs_gridded.html>`_
+    .. [3] `ACIS Web Services <http://www.rcc-acis.org/docs_webservices.html>`_
+
+    Examples
+    --------
+    >>> from pvlib.iotools import get_acis_mpe
+    >>> df, meta = get_acis_mpe(40, -80, '2020-01-01', '2020-12-31')
+    """
+    elems = [
+        # only precipitation is supported in this dataset
+        {"name": "pcpn", "interval": "dly", "units": "mm"},
+    ]
+    params = {
+        'loc': f"{longitude},{latitude}",
+        'grid': "2",
+        'elems': elems,
+        'meta': ["ll"],  # "elev" is not supported for this dataset
+    }
+    df, meta = _get_acis(start, end, params, map_variables, url, **kwargs)
+    df = df.replace(-999, np.nan)
+    return df, meta
+
+
+def get_acis_station_data(station, start, end, trace_val=0.001,
+                          map_variables=True,
+                          url="https://data.rcc-acis.org/StnData", **kwargs):
+    """
+    Retrieve weather station climate records via the Applied Climate
+    Information System (ACIS).
+
+    ACIS [1]_, [2]_ aggregates and provides access to climate data
+    from many underlying sources.  This function retrieves measurements
+    from ground stations belonging to various global networks.
+
+    This function can query data from stations all over the world.
+    The stations available in a given area can be listed using
+    :py:func:`get_acis_available_stations`.
+
+    Parameters
+    ----------
+    station : str
+        Identifier code for the station to query. Identifiers from many
+        station networks are accepted, including WBAN, COOP, FAA, WMO, GHCN,
+        and others.  See [1]_ and [2]_ for details.
+    start : datetime-like
+        First day of the requested period
+    end : datetime-like
+        Last day of the requested period
+    map_variables : bool, default True
+        When True, rename data columns and metadata keys to pvlib variable
+        names where applicable. See variable :const:`VARIABLE_MAP`.
+    trace_val : float, default 0.001
+        Value to replace "trace" values in the precipitation data
+    url : str, default: 'https://data.rcc-acis.org/GridData'
+        API endpoint URL
+    kwargs:
+        Optional parameters passed to ``requests.post``.
+
+    Returns
+    -------
+    data : pandas.DataFrame
+        Daily precipitation [mm], temperature [Celsius], snow [mm], and
+        degree day [Celsius-days] data
+    metadata : dict
+        station metadata
+
+    Raises
+    ------
+    requests.HTTPError
+        A message from the ACIS server if the request is rejected
+
+    See Also
+    --------
+    get_acis_available_stations
+
+    References
+    ----------
+    .. [1] `ACIS Web Services <http://www.rcc-acis.org/docs_webservices.html>`_
+    .. [2] `ACIS Metadata <http://www.rcc-acis.org/docs_metadata.html>`_
+
+    Examples
+    --------
+    >>> # Using an FAA code (Chicago O'Hare airport)
+    >>> from pvlib.iotools import get_acis_station_data
+    >>> df, meta = get_acis_station_data('ORD', '2020-01-01', '2020-12-31')
+    >>>
+    >>> # Look up available stations in a lat/lon rectangle, with data
+    >>> # available in the specified date range:
+    >>> from pvlib.iotools import get_acis_available_stations
+    >>> stations = get_acis_available_stations([39.5, 40.5], [-80.5, -79.5],
+    ...                                        '2020-01-01', '2020-01-03')
+    >>> stations['sids'][0]
+    ['369367 2', 'USC00369367 6', 'WYNP1 7']
+    >>> df, meta = get_acis_station_data('369367', '2020-01-01', '2020-01-03')
+    """
+    elems = [
+        {"name": "maxt", "interval": "dly", "units": "degreeC"},
+        {"name": "mint", "interval": "dly", "units": "degreeC"},
+        {"name": "avgt", "interval": "dly", "units": "degreeC"},
+        {"name": "obst", "interval": "dly", "units": "degreeC"},
+        {"name": "pcpn", "interval": "dly", "units": "mm"},
+        {"name": "snow", "interval": "dly", "units": "cm"},
+        {"name": "snwd", "interval": "dly", "units": "cm"},
+        {"name": "cdd", "interval": "dly", "units": "degreeC"},
+        {"name": "hdd", "interval": "dly", "units": "degreeC"},
+        {"name": "gdd", "interval": "dly", "units": "degreeC"},
+    ]
+    params = {
+        'sid': str(station),
+        'elems': elems,
+        'meta': ('name,state,sids,sid_dates,ll,elev,uid,county,'
+                 'climdiv,valid_daterange,tzo,network')
+    }
+    df, metadata = _get_acis(start, end, params, map_variables, url, **kwargs)
+    df = df.replace("M", np.nan)
+    df = df.replace("T", trace_val)
+    df = df.astype(float)
+    return df, metadata
+
+
+def get_acis_available_stations(latitude_range, longitude_range,
+                                start=None, end=None,
+                                url="https://data.rcc-acis.org/StnMeta",
+                                **kwargs):
+    """
+    List weather stations in a given area available from the
+    Applied Climate Information System (ACIS).
+
+    The ``sids`` returned by this function can be used with
+    :py:func:`get_acis_station_data` to retrieve weather measurements
+    from the station.
+
+    Parameters
+    ----------
+    latitude_range : list
+        A 2-element list of [southern bound, northern bound]
+        in decimal degrees, between -90 and 90, north is positive
+    longitude_range : list
+        A 2-element list of [western bound, eastern bound]
+        in decimal degrees, between -180 and 180, east is positive
+    start : datetime-like, optional
+        If specified, return only stations that have data between ``start`` and
+        ``end``.  If not specified, all stations in the region are returned.
+    end : datetime-like, optional
+        See ``start``
+    url : str, default: 'https://data.rcc-acis.org/StnMeta'
+        API endpoint URL
+    kwargs:
+        Optional parameters passed to ``requests.post``.
+
+    Returns
+    -------
+    stations : pandas.DataFrame
+        A dataframe of station metadata, one row per station.
+        The ``sids`` column contains IDs that can be used with 
+        :py:func:`get_acis_station_data`.
+
+    Raises
+    ------
+    requests.HTTPError
+        A message from the ACIS server if the request is rejected
+
+    See Also
+    --------
+    get_acis_station_data
+
+    References
+    ----------
+    .. [1] `ACIS Web Services <http://www.rcc-acis.org/docs_webservices.html>`_
+    .. [2] `ACIS Metadata <http://www.rcc-acis.org/docs_metadata.html>`_
+
+    Examples
+    --------
+    >>> # Look up available stations in a lat/lon rectangle, with data
+    >>> # available in the specified date range:
+    >>> from pvlib.iotools import get_acis_available_stations
+    >>> stations = get_acis_available_stations([39.5, 40.5], [-80.5, -79.5],
+    ...                                        '2020-01-01', '2020-01-03')
+    >>> stations['sids'][0]
+    ['369367 2', 'USC00369367 6', 'WYNP1 7']
+    """
+    bbox = "{},{},{},{}".format(
+        longitude_range[0],
+        latitude_range[0],
+        longitude_range[1],
+        latitude_range[1],
+    )
+    params = {
+        "bbox": bbox,
+        "meta": ("name,state,sids,sid_dates,ll,elev,"
+                 "uid,county,climdiv,tzo,network"),    
+    }
+    if start is not None and end is not None:
+        params['elems'] = ['maxt', 'mint', 'avgt', 'obst',
+                           'pcpn', 'snow', 'snwd']
+        params['sdate'] = pd.to_datetime(start).strftime('%Y-%m-%d')
+        params['edate'] = pd.to_datetime(end).strftime('%Y-%m-%d')
+
+    response = requests.post(url,
+                             json=params,
+                             headers={"Content-Type": "application/json"},
+                             **kwargs)
+    response.raise_for_status()
+    payload = response.json()
+    if "error" in payload:
+        raise requests.HTTPError(payload['error'], response=response)
+
+    metadata = payload['meta']
+    for station_record in metadata:
+        station_record['altitude'] = station_record.pop('elev')
+        station_record['longitude'], station_record['latitude'] = \
+            station_record.pop('ll')
+
+    df = pd.DataFrame(metadata)
+    return df
diff --git a/pvlib/tests/iotools/test_acis.py b/pvlib/tests/iotools/test_acis.py
new file mode 100644
index 0000000000..8458e9b930
--- /dev/null
+++ b/pvlib/tests/iotools/test_acis.py
@@ -0,0 +1,213 @@
+"""
+tests for :mod:`pvlib.iotools.acis`
+"""
+
+import pandas as pd
+import numpy as np
+import pytest
+from pvlib.iotools import (
+    get_acis_prism, get_acis_nrcc, get_acis_mpe,
+    get_acis_station_data, get_acis_available_stations
+)
+from ..conftest import RERUNS, RERUNS_DELAY, assert_frame_equal
+from requests import HTTPError
+
+
+@pytest.mark.remote_data
+@pytest.mark.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
+def test_get_acis_prism():
+    # map_variables=True
+    df, meta = get_acis_prism(40.001, -80.001, '2020-01-01', '2020-01-02')
+    expected = pd.DataFrame(
+        [
+            [0.5, 5, 0, 2.5, 0, 62, 0],
+            [0, 5, -3, 1, 0, 64, 0]
+        ],
+        columns=['precipitation', 'temp_air_max', 'temp_air_min',
+                 'temp_air_average', 'cooling_degree_days',
+                 'heating_degree_days', 'growing_degree_days'],
+        index=pd.to_datetime(['2020-01-01', '2020-01-02']),
+    )
+    assert_frame_equal(df, expected)
+    expected_meta = {'latitude': 40, 'longitude': -80, 'altitude': 298.0944}
+    assert meta == expected_meta
+
+    # map_variables=False
+    df, meta = get_acis_prism(40.001, -80.001, '2020-01-01', '2020-01-02',
+                              map_variables=False)
+    expected.columns = ['pcpn', 'maxt', 'mint', 'avgt', 'cdd', 'hdd', 'gdd']
+    assert_frame_equal(df, expected)
+    expected_meta = {'lat': 40, 'lon': -80, 'elev': 298.0944}
+    assert meta == expected_meta
+
+
+@pytest.mark.remote_data
+@pytest.mark.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
+@pytest.mark.parametrize('grid, expected', [
+    (1, [[0.51, 5, 0, 2.5, 0, 62, 0]]),
+    (3, [[0.51, 5, -1, 2.0, 0, 63, 0]])
+])
+def test_get_acis_nrcc(grid, expected):
+    # map_variables=True
+    df, meta = get_acis_nrcc(40.001, -80.001, '2020-01-01', '2020-01-01', grid)
+    expected = pd.DataFrame(
+        expected,
+        columns=['precipitation', 'temp_air_max', 'temp_air_min',
+                 'temp_air_average', 'cooling_degree_days',
+                 'heating_degree_days', 'growing_degree_days'],
+        index=pd.to_datetime(['2020-01-01']),
+    )
+    assert_frame_equal(df, expected)
+    expected_meta = {'latitude': 40., 'longitude': -80., 'altitude': 356.9208}
+    assert meta == pytest.approx(expected_meta)
+
+    # map_variables=False
+    df, meta = get_acis_nrcc(40.001, -80.001, '2020-01-01', '2020-01-01', grid,
+                             map_variables=False)
+    expected.columns = ['pcpn', 'maxt', 'mint', 'avgt', 'cdd', 'hdd', 'gdd']
+    assert_frame_equal(df, expected)
+    expected_meta = {'lat': 40., 'lon': -80., 'elev': 356.9208}
+    assert meta == pytest.approx(expected_meta)
+
+
+@pytest.mark.remote_data
+@pytest.mark.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
+def test_get_acis_nrcc_error():
+    with pytest.raises(HTTPError, match='invalid grid'):
+        # 50 is not a valid dataset (or "grid", in ACIS lingo)
+        _ = get_acis_nrcc(40, -80, '2012-01-01', '2012-01-01', 50)
+
+
+@pytest.mark.remote_data
+@pytest.mark.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
+def test_get_acis_mpe():
+    # map_variables=True
+    df, meta = get_acis_mpe(40.001, -80.001, '2020-01-01', '2020-01-02')
+    expected = pd.DataFrame(
+        {'precipitation': [0.4, 0.0]},
+        index=pd.to_datetime(['2020-01-01', '2020-01-02']),
+    )
+    assert_frame_equal(df, expected)
+    expected_meta = {'latitude': 40.0083, 'longitude': -79.9653}
+    assert meta == expected_meta
+
+    # map_variables=False
+    df, meta = get_acis_mpe(40.001, -80.001, '2020-01-01', '2020-01-02',
+                            map_variables=False)
+    expected.columns = ['pcpn']
+    assert_frame_equal(df, expected)
+    expected_meta = {'lat': 40.0083, 'lon': -79.9653}
+    assert meta == expected_meta
+
+
+@pytest.mark.remote_data
+@pytest.mark.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
+def test_get_acis_station_data():
+    # map_variables=True
+    df, meta = get_acis_station_data('ORD', '2020-01-10', '2020-01-12',
+                                     trace_val=-99)
+    expected = pd.DataFrame(
+        [[10., 2., 6., np.nan, 21.34, 0., 0., 0., 59., 0.],
+         [3., -4., -0.5, np.nan, 9.4, 5.3, 0., 0., 65., 0.],
+         [-1., -5., -3., np.nan, -99, -99, 5., 0., 68., 0.]],
+        columns=['temp_air_max', 'temp_air_min', 'temp_air_average',
+                 'temp_air_observation', 'precipitation', 'snowfall',
+                 'snowdepth', 'cooling_degree_days',
+                 'heating_degree_days', 'growing_degree_days'],
+        index=pd.to_datetime(['2020-01-10', '2020-01-11', '2020-01-12']),
+    )
+    assert_frame_equal(df, expected)
+    expected_meta = {
+        'uid': 48,
+        'state': 'IL',
+        'name': 'CHICAGO OHARE INTL AP',
+        'altitude': 204.8256,
+        'latitude': 41.96017,
+        'longitude': -87.93164
+    }
+    expected_meta = {
+        'valid_daterange': [
+            ['1958-11-01', '2023-06-15'],
+            ['1958-11-01', '2023-06-15'],
+            ['1958-11-01', '2023-06-15'],
+            [],
+            ['1958-11-01', '2023-06-15'],
+            ['1958-11-01', '2023-06-15'],
+            ['1958-11-01', '2023-06-15'],
+            ['1958-11-01', '2023-06-15'],
+            ['1958-11-01', '2023-06-15'],
+            ['1958-11-01', '2023-06-15']
+        ],
+        'name': 'CHICAGO OHARE INTL AP',
+        'sids': ['94846 1', '111549 2', 'ORD 3', '72530 4', 'KORD 5',
+                 'USW00094846 6', 'ORD 7', 'USW00094846 32'],
+        'county': '17031',
+        'state': 'IL',
+        'climdiv': 'IL02',
+        'uid': 48,
+        'tzo': -6.0,
+        'sid_dates': [
+            ['94846 1', '1989-01-19', '9999-12-31'],
+            ['94846 1', '1958-10-30', '1989-01-01'],
+            ['111549 2', '1989-01-19', '9999-12-31'],
+            ['111549 2', '1958-10-30', '1989-01-01'],
+            ['ORD 3', '1989-01-19', '9999-12-31'],
+            ['ORD 3', '1958-10-30', '1989-01-01'],
+            ['72530 4', '1989-01-19', '9999-12-31'],
+            ['72530 4', '1958-10-30', '1989-01-01'],
+            ['KORD 5', '1989-01-19', '9999-12-31'],
+            ['KORD 5', '1958-10-30', '1989-01-01'],
+            ['USW00094846 6', '1989-01-19', '9999-12-31'],
+            ['USW00094846 6', '1958-10-30', '1989-01-01'],
+            ['ORD 7', '1989-01-19', '9999-12-31'],
+            ['ORD 7', '1958-10-30', '1989-01-01'],
+            ['USW00094846 32', '1989-01-19', '9999-12-31'],
+            ['USW00094846 32', '1958-10-30', '1989-01-01']],
+        'altitude': 204.8256,
+        'longitude': -87.93164,
+        'latitude': 41.96017
+    }
+    # don't check valid dates since they get extended every day
+    meta.pop("valid_daterange")
+    expected_meta.pop("valid_daterange")
+    assert meta == expected_meta
+
+    # map_variables=False
+    df, meta = get_acis_station_data('ORD', '2020-01-10', '2020-01-12',
+                                     trace_val=-99, map_variables=False)
+    expected.columns = ['maxt', 'mint', 'avgt', 'obst', 'pcpn', 'snow',
+                        'snwd', 'cdd', 'hdd', 'gdd']
+    assert_frame_equal(df, expected)
+    expected_meta['lat'] = expected_meta.pop('latitude')
+    expected_meta['lon'] = expected_meta.pop('longitude')
+    expected_meta['elev'] = expected_meta.pop('altitude')
+    meta.pop("valid_daterange")
+    assert meta == expected_meta
+
+
+@pytest.mark.remote_data
+@pytest.mark.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
+def test_get_acis_available_stations():
+    # use a very narrow bounding box to hopefully make this test less likely
+    # to fail due to new stations being added in the future
+    lat, lon = 39.8986, -80.1656
+    stations = get_acis_available_stations([lat - 0.0001, lat + 0.0001],
+                                           [lon - 0.0001, lon + 0.0001])
+    assert len(stations) == 1
+    station = stations.iloc[0]
+
+    # test the more relevant values
+    assert station['name'] == 'WAYNESBURG 1 E'
+    assert station['sids'] == ['369367 2', 'USC00369367 6', 'WYNP1 7']
+    assert station['state'] == 'PA'
+    assert station['altitude'] == 940.
+    assert station['tzo'] == -5.0
+    assert station['latitude'] == lat
+    assert station['longitude'] == lon
+
+    # check that start/end work as filters
+    stations = get_acis_available_stations([lat - 0.0001, lat + 0.0001],
+                                           [lon - 0.0001, lon + 0.0001],
+                                           start='1900-01-01',
+                                           end='1900-01-02')
+    assert stations.empty