diff --git a/.gitignore b/.gitignore
index 597dc5c0..7b2841d1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -30,7 +30,6 @@ pyrealm.egg-info
 
 # Data
 pyrealm_build_data/inputs_data_24.25.nc
-pyrealm_build_data/eda.py
 
 # Profiling
 prof/
diff --git a/poetry.lock b/poetry.lock
index b7876eff..a0f572a7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3431,4 +3431,4 @@ xarray = [
 zipp = [
     {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
     {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
-]
+]
\ No newline at end of file
diff --git a/pyrealm_build_data/data_model.nc b/pyrealm_build_data/data_model.nc
new file mode 100644
index 00000000..27e46de6
Binary files /dev/null and b/pyrealm_build_data/data_model.nc differ
diff --git a/pyrealm_build_data/synth_data.py b/pyrealm_build_data/synth_data.py
new file mode 100644
index 00000000..4aff6334
--- /dev/null
+++ b/pyrealm_build_data/synth_data.py
@@ -0,0 +1,107 @@
+"""This script uses a parametrized model to compress the input dataset.
+
+It fits a time series model to the input data and stores the model parameters.
+The dataset can then be reconstructed from the model parameters using the `reconstruct`
+function, provided with a custom time index.
+"""
+from typing import Sequence
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+
+def make_time_features(t: Sequence[float]) -> pd.DataFrame:
+    """Make time features for a given time index.
+
+    The model can be written as
+    g(t) = a₀ + a₁ t + ∑_i b_i sin(2π f_i t) + c_i cos(2π f_i t),
+    where t is the time index, f_i are the frequencies, and a₀, a₁, b_i, c_i are the
+    model parameters.
+
+    Args:
+        t: An array of datetime values.
+    """
+    dt = pd.to_datetime(t).rename("time")
+    df = pd.DataFrame(index=dt).assign(const=1.0)
+
+    df["linear"] = (dt - pd.Timestamp("2000-01-01")) / pd.Timedelta("365.25d")
+
+    for f in [730.5, 365.25, 12, 6, 4, 3, 2, 1, 1 / 2, 1 / 3, 1 / 4, 1 / 6]:
+        df[f"freq_{f:.2f}_sin"] = np.sin(2 * np.pi * f * df["linear"])
+        df[f"freq_{f:.2f}_cos"] = np.cos(2 * np.pi * f * df["linear"])
+
+    return df
+
+
+def fit_ts_model(da: xr.DataArray, fs: pd.DataFrame) -> xr.DataArray:
+    """Fit a time series model to the data.
+
+    Args:
+        da: A DataArray with the input data.
+        fs: A DataFrame with the time features.
+    """
+    print("Fitting", da.name)
+
+    da = da.isel(time=slice(None, None, 4))  # downsample along time
+    da = da.dropna("time", how="all")
+    da = da.fillna(da.mean("time"))
+    df = da.to_series().unstack("time").T
+
+    Y = df.values  # (times, locs)
+    X = fs.loc[df.index].values  # (times, feats)
+    A, res, *_ = np.linalg.lstsq(X, Y, rcond=None)  # (feats, locs)
+
+    loss = np.mean(res) / len(X) / np.var(Y)
+    pars = pd.DataFrame(A.T, index=df.columns, columns=fs.columns)
+
+    print("Loss:", loss)
+    return pars.to_xarray().to_dataarray()
+
+
+def reconstruct(
+    ds: xr.Dataset, dt: Sequence[float], bounds: dict | None = None
+) -> xr.Dataset:
+    """Reconstruct the full dataset from the model parameters.
+
+    Args:
+        ds: A Dataset with the model parameters.
+        dt: An array of datetime values.
+        bounds: A dictionary with the bounds for the reconstructed variables.
+    """
+    if bounds is None:
+        bounds = dict(
+            temp=(-25, 80),
+            patm=(3e4, 11e4),
+            vpd=(0, 1e4),
+            co2=(0, 1e3),
+            fapar=(0, 1),
+            ppfd=(0, 1e4),
+        )
+    x = make_time_features(dt).to_xarray().to_dataarray()
+    ds = xr.Dataset({k: a @ x for k, a in ds.items()})
+    ds = xr.Dataset({k: a.clip(*bounds[k]) for k, a in ds.items()})
+    return ds
+
+
+if __name__ == "__main__":
+    ds = xr.open_dataset("pyrealm_build_data/inputs_data_24.25.nc")
+
+    # drop locations with all NaNs (for any variable)
+    mask = ~ds.isnull().all("time").to_dataarray().any("variable")
+    ds = ds.where(mask, drop=True)
+
+    special_time_features = dict(
+        patm=["const"],
+        co2=["const", "linear"],
+    )
+
+    features = make_time_features(ds.time)
+
+    model = xr.Dataset()
+    for k in ds.data_vars:
+        cols = special_time_features.get(k, features.columns)
+        model[k] = fit_ts_model(ds[k], features[cols])
+
+    model = model.fillna(0.0)
+    model.to_netcdf("pyrealm_build_data/data_model.nc")
diff --git a/tests/regression/data/test_synth_data.py b/tests/regression/data/test_synth_data.py
new file mode 100644
index 00000000..6e582689
--- /dev/null
+++ b/tests/regression/data/test_synth_data.py
@@ -0,0 +1,46 @@
+"""Test the quality of the synthetic data generated from the model parameters."""
+
+import numpy as np
+import pytest
+import xarray as xr
+
+try:
+    DATASET = xr.open_dataset("pyrealm_build_data/inputs_data_24.25.nc")
+    VARS = DATASET.data_vars
+except ValueError:
+    pytest.skip("Original LFS dataset not checked out.", allow_module_level=True)
+
+
+def r2_score(y_true: xr.DataArray, y_pred: xr.DataArray) -> float:
+    """Compute the R2 score."""
+    SSE = ((y_true - y_pred) ** 2).sum()
+    SST = ((y_true - y_true.mean()) ** 2).sum()
+    return 1 - SSE / SST
+
+
+@pytest.fixture
+def syndata(modelpath="pyrealm_build_data/data_model.nc"):
+    """The synthetic dataset."""
+    from pyrealm_build_data.synth_data import reconstruct
+
+    model = xr.open_dataset(modelpath)
+    ts = xr.date_range("2012-01-01", "2018-01-01", freq="12h")
+    return reconstruct(model, ts)
+
+
+@pytest.fixture
+def dataset(syndata):
+    """The original dataset."""
+    return DATASET.sel(time=syndata.time)
+
+
+@pytest.mark.parametrize("var", VARS)
+def test_synth_data_quality(dataset, syndata, var):
+    """Test the quality of the synthetic data."""
+    times = syndata.time[np.random.choice(syndata.time.size, 1000, replace=False)]
+    lats = syndata.lat[np.random.choice(syndata.lat.size, 100, replace=False)]
+    t = dataset[var].sel(lat=lats, time=times)
+    p = syndata[var].sel(lat=lats, time=times)
+    s = r2_score(t, p)
+    print(f"R2 score for {var} is {s:.2f}")
+    assert s > 0.85