diff --git a/docs/examples/twri.py b/docs/examples/twri.py
index 9e36d7f..c2ac100 100644
--- a/docs/examples/twri.py
+++ b/docs/examples/twri.py
@@ -285,7 +285,8 @@ def plot_head(head, workspace):
 nc_fpth = workspace / "twri.input.nc"
 gwf.netcdf_file = nc_fpth
 
-ds = gwf.to_xarray(format="structured")
+nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf)
+ds = nc_model.to_xarray()
 ds.to_netcdf(nc_fpth)
 
 with flopy4.mf6.write_context.WriteContext(use_netcdf=True):
@@ -303,7 +304,8 @@ def plot_head(head, workspace):
 nc_fpth = workspace / "twri.input.nc"
 gwf.netcdf_file = nc_fpth
 
-ds = gwf.to_xarray(format="layered")
+nc_model = flopy4.mf6.netcdf.NetCDFModel.from_model(gwf, mesh="layered")
+ds = nc_model.to_xarray()
 ds.to_netcdf(nc_fpth)
 
 with flopy4.mf6.write_context.WriteContext(use_netcdf=True):
diff --git a/flopy4/mf6/__init__.py b/flopy4/mf6/__init__.py
index 5e835eb..a146e1a 100644
--- a/flopy4/mf6/__init__.py
+++ b/flopy4/mf6/__init__.py
@@ -12,11 +12,12 @@
 from flopy4.mf6.component import Component
 from flopy4.mf6.converter import structure, unstructure
 from flopy4.mf6.ims import Ims
+from flopy4.mf6.netcdf import NetCDFModel
 from flopy4.mf6.simulation import Simulation
 from flopy4.mf6.tdis import Tdis
 from flopy4.uio import DEFAULT_REGISTRY
 
-__all__ = ["gwf", "simulation", "solution", "utils", "Ims", "Tdis", "Simulation"]
+__all__ = ["gwf", "simulation", "solution", "utils", "Ims", "NetCDFModel", "Tdis", "Simulation"]
 
 
 class WriteError(Exception):
diff --git a/flopy4/mf6/converter/egress/dataset.py b/flopy4/mf6/converter/egress/dataset.py
deleted file mode 100644
index 9d05ec6..0000000
--- a/flopy4/mf6/converter/egress/dataset.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import numpy as np
-import xarray as xr
-import xattree
-
-from flopy4.mf6.constants import FILL_DNODATA, FILL_FLOAT64, FILL_INT32, FILL_INT64
-from flopy4.mf6.model import Model
-from flopy4.mf6.package import Package
-
-
-# TODO: longname, auxiliary
-def xarray_flat(dt: xr.DataTree, mesh_type: str | None = None):
-    mesh_type = mesh_type.lower() if mesh_type is not None else None
-    model = dt.attrs["host"]  # type: ignore
-    assert isinstance(model, Model)
-
-    mname = model.name  # type: ignore
-    mtype = model.__class__.__name__.lower()
-    nlay = model.data.dims["nlay"]  # type: ignore
-
-    # set grid_type # TODO dis vs disv child?
-    if "nrow" in model.data.dims and "ncol" in model.data.dims:  # type: ignore
-        grid_type = "structured"
-    elif "ncpl" in model.data.dims:  # type: ignore
-        grid_type = "vertext"
-    else:
-        pass  # raise?
-
-    def _dims(shape, layer=False):
-        dimmap = {
-            "time": model.data.dims["nper"],
-            "z": model.data.dims["nlay"],
-            "y": model.data.dims["nrow"],
-            "nmesh_face": model.data.dims["ncpl"],
-            "x": model.data.dims["ncol"],
-        }
-
-        shp = list(shape)
-        if layer:
-            idx = 1 if shape[0] == "time" else 0
-            shp.insert(idx, "z")
-        return [dimmap[s] for s in shp]
-
-    def _encode(varname, shape, dataset):
-        if dataset[varname].dtype == np.float64:
-            if "time" in shape:
-                dataset[varname].fillna(FILL_DNODATA)
-            else:
-                dataset[varname].fillna(FILL_FLOAT64)
-        elif dataset[varname].dtype == np.int64:
-            dataset[varname].fillna(FILL_INT64)
-        elif dataset[varname].dtype == np.int32:
-            dataset[varname].fillna(FILL_INT32)
-        return dataset
-
-    def _add_gridvar(field_name, package, multi, data, dataset):
-        ptype = package.__class__.__name__.lower()
-        shape = ["time"] if "nper" in data[field_name].dims else []
-        if "nodes" in data[field_name].dims:
-            shape += ["z", "y", "x"]
-        elif "ncpl" in data[field_name].dims:
-            shape += ["y", "x"]
-        else:
-            if "nlay" in data[field_name].dims:
-                shape.append("z")
-            if "nrow" in data[field_name].dims:
-                shape.append("y")
-            if "ncol" in data[field_name].dims:
-                shape.append("x")
-        if multi:
-            varname = f"{package.name}_{field_name}"
-            mf6_input = f"{mname}/{package.name}/{field_name}"
-        else:
-            varname = f"{ptype}_{field_name}"
-            mf6_input = f"{mname}/{ptype}/{field_name}"
-
-        var_d = {varname: (shape, data[field_name].values.reshape(_dims(shape)))}
-        dataset = dataset.assign(var_d)
-        dataset[varname].attrs["modflow_input"] = mf6_input
-        dataset = _encode(varname, shape, dataset)
-
-        return dataset
-
-    def _add_layered_gridvars(field_name, package, multi, data, dataset):
-        ptype = package.__class__.__name__.lower()
-        shape = ["time"] if "nper" in data[field_name].dims else []
-        if (
-            "nodes" in data[field_name].dims
-            or "ncpl" in data[field_name].dims
-            or ("nrow" in data[field_name].dims and "ncol" in data[field_name].dims)
-        ):
-            shape.append("nmesh_face")
-        elif "nrow" in data[field_name].dims:
-            shape.append("y")
-        elif "ncol" in data[field_name].dims:
-            shape.append("x")
-        if multi:
-            mf6_input = f"{mname}/{package.name}/{field_name}"
-            basename = f"{package.name}_{field_name}"
-        else:
-            mf6_input = f"{mname}/{ptype}/{field_name}"
-            basename = f"{ptype}_{field_name}"
-
-        if (
-            data[field_name].dims == ("nper", "nodes")
-            or "nlay" in data[field_name].dims
-            or "nodes" in data[field_name].dims
-        ):
-            for layer in range(nlay):
-                varname = f"{basename}_l{layer + 1}"
-                if data[field_name].dims == ("nper", "nodes"):
-                    var_d = {
-                        varname: (
-                            shape,
-                            data[field_name].values.reshape(_dims(shape, True))[:, layer, :],
-                        )
-                    }
-                elif "nlay" in data[field_name].dims:
-                    var_d = {varname: (shape, data[field_name].values[layer, :, :].ravel())}
-                elif "nodes" in data[field_name].dims:
-                    var_d = {
-                        varname: (
-                            shape,
-                            data[field_name].values.reshape(_dims(shape, True))[layer, :].ravel(),
-                        )
-                    }
-                dataset = dataset.assign(var_d)
-                dataset[varname].attrs["modflow_input"] = mf6_input
-                dataset[varname].attrs["layer"] = layer + 1
-                dataset = _encode(varname, shape, dataset)
-        else:
-            varname = basename
-            var_d = {varname: (shape, data[field_name].values.reshape(_dims(shape)))}
-            dataset = dataset.assign(var_d)
-            dataset[varname].attrs["modflow_input"] = mf6_input
-            dataset = _encode(varname, shape, dataset)
-        return dataset
-
-    ds = xr.Dataset()
-    ds.attrs["modflow_model"] = f"{mtype}6: {mname}"
-    ds.attrs["modflow_grid"] = f"{grid_type}"
-    if mesh_type is not None:
-        ds.attrs["mesh"] = mesh_type
-
-    for c in model.children:  # type: ignore
-        package = model.children[c]  # type: ignore
-        assert isinstance(package, Package)
-        xatspec = xattree.get_xatspec(type(package))
-
-        if hasattr(package, "multi_package"):
-            multi = package.multi_package
-        else:
-            multi = False
-
-        data = xattree.asdict(package)
-
-        for block_name, block in package.dfn.blocks.items():
-            if block_name != "griddata" and block_name != "period":
-                continue
-            for field_name in block.keys():
-                if (
-                    data[field_name] is None
-                    or field_name not in xatspec.arrays
-                    or not hasattr(xatspec.arrays[field_name], "metadata")
-                    or "netcdf" not in xatspec.arrays[field_name].metadata  # type: ignore
-                    or not xatspec.arrays[field_name].metadata["netcdf"]  # type: ignore
-                ):
-                    continue
-
-                if mesh_type is None:
-                    ds = _add_gridvar(field_name, package, multi, data, ds)
-                elif mesh_type == "layered":
-                    ds = _add_layered_gridvars(field_name, package, multi, data, ds)
-
-    return ds
diff --git a/flopy4/mf6/model.py b/flopy4/mf6/model.py
index 985370a..a3ebd97 100644
--- a/flopy4/mf6/model.py
+++ b/flopy4/mf6/model.py
@@ -1,6 +1,5 @@
 from abc import ABC
 
-import xarray as xr
 from xattree import xattree
 
 from flopy4.mf6.context import Context
@@ -10,15 +9,3 @@
 class Model(Context, ABC):
     def default_filename(self) -> str:
         return f"{self.name}.nam"  # type: ignore
-
-    def to_xarray(self, format: str | None = None) -> xr.Dataset:
-        from flopy4.mf6.converter.egress.dataset import xarray_flat
-
-        dt = super().to_xarray()
-        if format is not None:
-            mesh_type = None
-            if format.lower() == "layered":
-                mesh_type = "layered"
-            return xarray_flat(dt, mesh_type)
-        else:
-            return dt
diff --git a/flopy4/mf6/netcdf.py b/flopy4/mf6/netcdf.py
new file mode 100644
index 0000000..59f2cd0
--- /dev/null
+++ b/flopy4/mf6/netcdf.py
@@ -0,0 +1,772 @@
+import abc
+
+import numpy as np
+import xarray as xr
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    ValidationError,
+    ValidationInfo,
+    field_validator,
+)
+from xattree import XatSpec, asdict, get_xatspec
+
+from flopy4.mf6.constants import FILL_DNODATA, FILL_FLOAT64, FILL_INT64
+from flopy4.mf6.model import Model
+from flopy4.mf6.package import Package
+from flopy4.version import __version__
+
+
+def metadata(attribute, key: str):
+    if hasattr(attribute, "metadata"):
+        meta = getattr(attribute, "metadata")
+        if key in meta:
+            return meta[key]
+    return None
+
+
+def _pkgclass(package_name: str) -> Package:
+    import flopy4
+
+    mtype, ptype = package_name.lower().split("-")
+    if not hasattr(flopy4.mf6, mtype):
+        raise AssertionError(f"not a valid mf6 component: {package_name}")
+    model = getattr(flopy4.mf6, mtype)
+    if not hasattr(model, ptype):
+        raise AssertionError(f"not a valid mf6 component: {package_name}")
+    module = getattr(model, ptype)
+    return getattr(module, ptype.capitalize())
+
+
+def multi_package(package_name: str) -> bool:
+    package = _pkgclass(package_name)
+    if hasattr(package, "multi_package"):
+        return getattr(package, "multi_package")
+    return False
+
+
+def get_spec(package_name: str) -> XatSpec:
+    return get_xatspec(_pkgclass(package_name))  # type: ignore
+
+
+def dimmap(gridtype: str, dims: list[int]) -> dict:
+    _map = {
+        "time": dims[0],
+        "z": dims[1],
+    }
+    if gridtype == "structured":
+        _map["y"] = dims[2]
+        _map["x"] = dims[3]
+        _map["nmesh_face"] = dims[2] * dims[3]
+    elif gridtype == "vertex":
+        _map["nmesh_face"] = dims[2]
+    return _map
+
+
+def lower(d: dict) -> dict:
+    return {k.lower(): v.lower() if isinstance(v, str) else v for k, v in d.items()}
+
+
+# Define an Abstract Base Class (interface)
+class NetCDFInput(abc.ABC):
+    @classmethod
+    @abc.abstractmethod
+    def from_dict(cls, meta: dict, context: dict | None):
+        """create new instance, validate against schema."""
+        pass
+
+    @abc.abstractmethod
+    def to_xarray(self) -> xr.Dataset:
+        """create xarray dataset."""
+        pass
+
+    @property
+    @abc.abstractmethod
+    def meta(self) -> dict:
+        """get meta dictionary property."""
+        pass
+
+    @abc.abstractmethod
+    def jsonschema(self) -> dict:
+        """create xarray dataset."""
+        pass
+
+
+class NetCDFModel(BaseModel, NetCDFInput):
+    attrs: "NetCDFModelAttrs"
+    packages: list["NetCDFPackage"] = Field(default_factory=list)
+
+    def model_post_init(self, __context) -> None:
+        self._context = __context
+
+    @classmethod
+    def from_dict(cls, meta, context=None):
+        try:
+            if context:
+                context = lower(context)
+            _meta = NetCDFModel._backfill_meta(meta, context)
+            inst = cls.model_validate(_meta, context=context)
+            inst._context |= context if context is not None else inst._context
+            return inst
+        except ValidationError:
+            raise
+
+    @classmethod
+    def from_model(cls, model: Model, mesh: str | None = None):
+        assert hasattr(model, "name")
+        assert hasattr(model, "data")
+
+        modeltype = model.__class__.__name__.lower()
+        attrs = {"title": f"{model.name.upper()} model input"}
+        packages = []
+        distype = None
+
+        if mesh is not None and mesh.lower() != "structured":
+            attrs["mesh"] = mesh.lower()
+
+        for c in model.children:  # type: ignore
+            package = model.children[c]  # type: ignore
+            packagetype = package.__class__.__name__.lower()
+            distype = packagetype if packagetype.startswith("dis") else distype
+            # TODO: auxiliary
+            p = {
+                "package_name": package.name,
+                "package_type": f"{modeltype}-{packagetype}",
+                "params": [],
+            }
+            xatspec = get_xatspec(type(package))
+            multi = package.multi_package if hasattr(package, "multi_package") else False
+            data = asdict(package)
+
+            for block_name, block in package.dfn.blocks.items():
+                if block_name != "griddata" and block_name != "period":
+                    continue
+                for field_name in block.keys():
+                    if (
+                        data[field_name] is None
+                        or field_name not in xatspec.arrays
+                        or not hasattr(xatspec.arrays[field_name], "metadata")
+                        or "netcdf" not in xatspec.arrays[field_name].metadata  # type: ignore
+                        or not xatspec.arrays[field_name].metadata["netcdf"]  # type: ignore
+                    ):
+                        continue
+
+                    p["params"].append({"name": field_name, "data": data[field_name].values})
+
+            if len(p["params"]) > 0:
+                packages.append(p)
+
+        dims = [
+            model.data.dims["nper"],
+            model.data.dims["nlay"],
+        ]
+
+        if distype == "dis":
+            dims.append(model.data.dims["nrow"])
+            dims.append(model.data.dims["ncol"])
+            gridtype = "structured"
+        elif distype == "disv":
+            dims.append(model.data.dims["ncpl"])
+            gridtype = "vertex"
+
+        return NetCDFModel.from_dict(
+            meta={
+                "modeltype": modeltype,
+                "modelname": model.name,
+                "gridtype": gridtype,
+                "attrs": attrs,
+                "packages": packages,
+            },
+            context={"dims": dims},
+        )
+
+    def to_xarray(self) -> xr.Dataset:
+        import datetime
+
+        dss = []
+        meta = self.model_dump(by_alias=True)
+        for p in self.packages:
+            dss.append(p.to_xarray())
+
+        ds = xr.merge(dss)
+
+        dt = datetime.datetime.now()
+        timestamp = dt.strftime("%m/%d/%Y %H:%M:%S")
+        meta["attrs"]["source"] = f"pyphoenix {__version__}"
+        meta["attrs"]["history"] = f"first created {timestamp}"
+        # TODO: conventions, etc
+
+        for a in meta["attrs"]:
+            if meta["attrs"][a] is not None:
+                ds.attrs[a] = meta["attrs"][a]
+        return ds
+
+    @property
+    def meta(self):
+        """meta property getter."""
+        return self.model_dump(by_alias=True)
+
+    def jsonschema(self) -> dict:
+        return self.model_json_schema()
+
+    @field_validator("attrs", mode="before")
+    @classmethod
+    def validate_attrs(cls, v: dict[str, str]) -> dict[str, str]:
+        """
+        validate model (dataset) scoped attributes dictionary
+        """
+        v = lower(v)
+        return v
+
+    @staticmethod
+    def _backfill_meta(meta: dict, context: dict, verbose: bool = True) -> dict:
+        _meta = dict(meta)
+
+        if "attrs" not in _meta:
+            _meta["attrs"] = {}
+
+        _meta = lower(_meta)
+        _meta["attrs"]["modflow_grid"] = _meta["gridtype"].lower()
+        _meta["attrs"]["modflow_model"] = (
+            f"{_meta['modeltype'].lower()}: {_meta['modelname'].lower()}"
+        )
+
+        _packages = []
+        for pkg in _meta["packages"]:
+            pkgctx = {"mesh": _meta["attrs"]["mesh"]} if "mesh" in _meta["attrs"] else {}
+            pkgctx["modelname"] = _meta["modelname"]
+            pkgctx["gridtype"] = _meta["gridtype"]
+            pkgctx |= context
+            _packages.append(NetCDFPackage.from_dict(pkg, context=pkgctx))
+        _meta["packages"] = _packages
+
+        return _meta
+
+
+class NetCDFModelAttrs(BaseModel):
+    # order of params dictates when data added to info dict
+    mesh: str | None = Field(default=None)
+    modflow_grid: str = Field()
+    modflow_model: str = Field()
+
+    model_config = ConfigDict(extra="allow")
+
+    @field_validator("mesh", mode="before")
+    @classmethod
+    def validate_mesh(cls, v: str | None, info: ValidationInfo) -> str | None:
+        """
+        validate model mesh attribute
+        """
+        if v is not None:
+            v = v.lower()
+            if v != "layered":
+                raise ValueError("only LAYERED mesh supported")
+            info.context["mesh"] = v  # type: ignore
+        return v
+
+    @field_validator("modflow_grid", mode="before")
+    @classmethod
+    def validate_modflow_grid(cls, v: str, info: ValidationInfo) -> str:
+        """
+        validate model modflow_grid attribute
+        """
+        v = v.lower()
+        dims = info.context.get("dims")  # type: ignore
+        if v == "structured":
+            if len(dims) != 4:
+                raise AssertionError(
+                    "expected 4 input dimensions [time, nlay, nrow, ncol]"
+                    " for structured discretization: {dims}"
+                )
+        elif v == "vertex":
+            if len(dims) != 3:
+                raise AssertionError(
+                    "expected 3 input dimensions [time, nlay, ncpl]"
+                    " for vertex discretization: {dims}"
+                )
+        info.context["gridtype"] = v  # type: ignore
+        return v
+
+    @field_validator("modflow_model", mode="before")
+    @classmethod
+    def validate_modflow_model(cls, v: str, info: ValidationInfo) -> str:
+        """
+        validate model modflow_model attribute
+        """
+        v = v.lower()
+        tokens = v.split(":")
+        if len(tokens) != 2:
+            raise ValueError(f"invalid modflow_model attribute: {v}")
+        modeltype = tokens[0].strip()
+        if modeltype[-1].isdigit():
+            modeltype = modeltype[:-1]
+        info.context["modeltype"] = modeltype  # type: ignore
+        info.context["modelname"] = tokens[1].strip()  # type: ignore
+        return v
+
+
+class NetCDFPackage(BaseModel, NetCDFInput):
+    package_name: str = Field()
+    package_type: str = Field()
+    params: list["NetCDFParam"] = Field(default_factory=list)
+    auxiliary: list[str] = Field(default_factory=list)
+
+    def model_post_init(self, __context) -> None:
+        self._context = __context
+
+        if len(self.auxiliary) > 0:
+            for p in self.params:
+                if p.name == "aux":
+                    p._context["auxiliary"] = self.auxiliary
+
+    @classmethod
+    def from_dict(cls, meta, context):
+        try:
+            if context:
+                context = lower(context)
+            _meta = NetCDFPackage._backfill_meta(meta, context)
+            inst = cls.model_validate(_meta, context=context)
+            inst._context |= context if context is not None else inst._context
+            return inst
+        except ValidationError:
+            raise
+
+    def to_xarray(self) -> xr.Dataset:
+        ds = []
+        for p in self.params:
+            ds.append(p.to_xarray())
+
+        return xr.merge(ds)
+
+    @property
+    def meta(self):
+        """meta property getter."""
+        return self.model_dump(by_alias=True)
+
+    def jsonschema(self) -> dict:
+        return self.model_json_schema()
+
+    @field_validator("package_name", mode="before")
+    @classmethod
+    def validate_package_name(cls, v: str, info: ValidationInfo) -> str:
+        """
+        validate package_name string
+        """
+        v = v.lower()
+        info.context["package_name"] = v  # type: ignore
+        return v
+
+    @field_validator("package_type", mode="before")
+    @classmethod
+    def validate_package_type(cls, v: str, info: ValidationInfo) -> str:
+        """
+        validate package_type string [<component>-<subcomponent>]
+        """
+        v = v.lower()
+        assert get_spec(v)
+        info.context["package_type"] = v  # type: ignore
+        return v
+
+    @field_validator("auxiliary", mode="before")
+    @classmethod
+    def validate_auxiliary(cls, v: list[str]) -> list[str]:
+        """
+        validate package auxiliary list
+        """
+        v = [aux.lower() for aux in v]
+        return v
+
+    @staticmethod
+    def _backfill_meta(meta: dict, context: dict, verbose: bool = True) -> dict:
+        _meta = dict(meta)
+
+        if "package_name" not in _meta or "package_type" not in _meta:
+            raise AssertionError(
+                "package missing required package_name or package_type attribute(s)."
+            )
+
+        auxiliary = _meta.get("auxiliary", None)
+
+        paramctx = dict(context)
+        paramctx["package_name"] = _meta["package_name"]
+        paramctx["package_type"] = _meta["package_type"]
+        if auxiliary is not None:
+            paramctx["auxiliary"] = auxiliary
+
+        spec = get_spec(paramctx["package_type"].lower())
+
+        dims = context.get("dims", None)
+        mesh = context.get("mesh", None)
+        if dims is None:
+            raise AssertionError("dimensions are required context")
+
+        def _add_layered_param(p):
+            for layer in range(dims[1]):
+                p["attrs"]["layer"] = layer + 1
+                _params.append(NetCDFParam.from_dict(p, context=paramctx))
+
+        _params = []
+        for p in _meta["params"]:
+            if "attrs" not in p:
+                p["attrs"] = {}
+
+            if p["name"].lower() == "aux" and (auxiliary is None or len(auxiliary) == 0):
+                raise AssertionError("AUX parameter requires auxiliary list input.")
+
+            shape = spec.arrays[p["name"]].dims
+            assert shape is not None
+            gridded = "nodes" in shape or "nlay" in shape
+
+            if not gridded or mesh is None:
+                assert "layer" not in p["attrs"]
+                if p["name"].lower() == "aux":
+                    for i, aux in enumerate(auxiliary):  # type: ignore
+                        p["attrs"]["modflow_iaux"] = i + 1
+                        _params.append(NetCDFParam.from_dict(p, context=paramctx))
+                else:
+                    _params.append(NetCDFParam.from_dict(p, context=paramctx))
+
+            else:
+                if p["name"].lower() == "aux":
+                    for i, aux in enumerate(auxiliary):  # type: ignore
+                        p["attrs"]["modflow_iaux"] = i + 1
+                        _add_layered_param(p)
+                else:
+                    _add_layered_param(p)
+
+        _meta["params"] = _params
+
+        return _meta
+
+
+class NetCDFParam(BaseModel, NetCDFInput):
+    name: str = Field()
+    shape: list[str] = Field(default_factory=list)
+    attrs: "NetCDFParamAttrs"
+    encodings: "NetCDFParamEncodings"
+    dtype: np.dtype = Field()
+    data: np.ndarray | None = None
+
+    # Allow Pydantic to handle non-native types (numpy)
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    def model_post_init(self, __context) -> None:
+        self._context = __context
+        self._context["dimmap"] = dimmap(self._context["gridtype"], self._context["dims"])
+
+    @classmethod
+    def from_dict(cls, meta, context):
+        try:
+            if context:
+                context = lower(context)
+                # TODO: verify required context
+                if (
+                    "modelname" not in context
+                    or "package_name" not in context
+                    or "package_type" not in context
+                    or "gridtype" not in context
+                    or "dims" not in context
+                ):
+                    raise AssertionError(
+                        "NetCDFParam incomplete context: modelanme, package_name, "
+                        "package_type, gridtype and dims are required."
+                    )
+                context["dimmap"] = dimmap(context["gridtype"], context["dims"])
+            _meta = NetCDFParam._backfill_meta(meta, context)
+            inst = cls.model_validate(_meta, context=context)
+            inst._context |= context if context is not None else inst._context
+            return inst
+        except ValidationError:
+            raise
+
+    def to_xarray(self) -> xr.Dataset:
+        meta = self.model_dump(by_alias=True)
+        package_name = self._context["package_name"]
+        package_type = self._context["package_type"]
+        mesh = self._context.get("mesh", None)
+        auxiliary = self._context.get("auxiliary", None)
+        ptype = package_type.split("-")[1].strip()
+        spec = get_spec(package_type)
+
+        ds = xr.Dataset()
+
+        basename = (
+            (
+                auxiliary[meta["attrs"]["modflow_iaux"] - 1]
+                if auxiliary is not None
+                else f"aux{meta['attrs']['modflow_iaux']}"
+            )
+            if meta["name"] == "aux"
+            else meta["name"]
+        )
+        param = (
+            basename
+            if (mesh is None or "layer" not in meta["attrs"] or meta["attrs"]["layer"] is None)
+            else f"{basename}_l{meta['attrs']['layer']}"
+        )
+        varname = f"{package_name}_{param}" if multi_package(package_type) else f"{ptype}_{param}"
+
+        if "data" in meta and meta["data"] is not None:
+            data = meta["data"]
+        else:
+            dims = [self._context["dimmap"][dim] for dim in meta["shape"]]
+            data = np.full(
+                dims,
+                meta["encodings"]["_FillValue"],
+                dtype=meta["dtype"],
+            )
+
+        var_d = {varname: (meta["shape"], data)}
+        ds = ds.assign(var_d)
+
+        for a in meta["attrs"]:
+            if meta["attrs"][a] is not None:
+                ds[varname].attrs[a] = meta["attrs"][a]
+        for e in meta["encodings"]:
+            if meta["encodings"][e] is not None:
+                ds[varname].encoding[e] = meta["encodings"][e]
+
+        return ds
+
+    @property
+    def meta(self):
+        """meta property getter."""
+        return self.model_dump(by_alias=True)
+
+    def jsonschema(self) -> dict:
+        return self.model_json_schema()
+
+    @field_validator("name", mode="before")
+    @classmethod
+    def validate_name(cls, v: str, info: ValidationInfo) -> str:
+        """
+        validate parameter name
+        """
+        v = v.lower()
+        package = info.context.get("package_type")  # type: ignore
+        spec = get_spec(package)
+
+        if v not in spec.arrays:
+            raise ValueError(f"param {v} not found in package {package}")
+        if not metadata(spec.arrays[v], "netcdf"):
+            raise ValueError(f"not a netcdf param: '{v}'")
+        return v
+
+    @field_validator("shape", mode="before")
+    @classmethod
+    def validate_shape(cls, v: list[str]) -> list[str]:
+        """
+        validate parameter shape
+        """
+        v = [dim.lower() if isinstance(dim, str) else dim for dim in v]
+        valid = ["time", "nmesh_face", "z", "y", "x"]
+        if not all(dim in valid for dim in v):
+            raise AssertionError(f"invalid param shape={v}. Valid dims={valid}.")
+        return v
+
+    @field_validator("attrs", mode="before")
+    @classmethod
+    def validate_attrs(cls, v: dict[str, int | str], info: ValidationInfo) -> dict[str, int | str]:
+        """
+        validate parameter attributes dictionary
+        """
+        v = lower(v)
+        param = info.data.get("name")
+        mesh = info.context.get("mesh")  # type: ignore
+        shape = info.data.get("shape")
+        assert shape
+        gridded = "nodes" in shape or "nlay" in shape
+        if gridded and mesh is not None and ("layer" not in v or v["layer"] is None):
+            raise AssertionError(f"expected layer attribute for mesh param '{param}'")
+        if param is not None and param == "aux" and "modflow_iaux" not in v:
+            raise AssertionError("expected modflow_iaux attribute for aux param")
+        return v
+
+    @field_validator("encodings", mode="before")
+    @classmethod
+    def validate_encodings(cls, v: dict[str, int | float | str]) -> dict[str, int | float | str]:
+        """
+        validate parameter encodings dictionary
+        """
+        return v
+
+    @field_validator("dtype", mode="before")
+    @classmethod
+    def validate_dtype(cls, v: np.dtype) -> np.dtype:
+        """
+        validate parameter dtype
+        """
+        if not (np.issubdtype(v, np.floating) or np.issubdtype(v, np.integer)):
+            raise AssertionError(f"invalid param dtype={v}, expected numpy numeric dtype.")
+        return v
+
+    @field_validator("data", mode="before")
+    @classmethod
+    def validate_data(cls, v: np.ndarray) -> np.ndarray:
+        """
+        validate parameter data
+        """
+        return v
+
+    @staticmethod
+    def _backfill_meta(meta: dict, context: dict, verbose: bool = True) -> dict:
+        _meta = dict(meta)
+        param = _meta["name"]
+        modelname = context["modelname"]
+        mesh = context.get("mesh", None)
+        spec = get_spec(context["package_type"])
+
+        if param not in spec.arrays:
+            raise ValueError(f"param {param} not found in package {context['package_type']}")
+
+        if "attrs" not in _meta:
+            _meta["attrs"] = {}
+        if "encodings" not in _meta:
+            _meta["encodings"] = {}
+
+        # add long_name to parameter attributes
+        _meta["attrs"]["long_name"] = metadata(spec.arrays[param], "longname")
+
+        def _structured_shape(dfn_shape):
+            shape = ["time"] if "nper" in dfn_shape else []
+            if "nodes" in dfn_shape:
+                shape += ["z", "y", "x"]
+            elif "ncpl" in dfn_shape:
+                shape += ["y", "x"]
+            else:
+                if "nlay" in dfn_shape:
+                    shape.append("z")
+                if "nrow" in dfn_shape:
+                    shape.append("y")
+                if "ncol" in dfn_shape:
+                    shape.append("x")
+            return shape
+
+        def _mesh_shape(dfn_shape):
+            shape = ["time"] if "nper" in dfn_shape else []
+            if (
+                "nodes" in dfn_shape
+                or "ncpl" in dfn_shape
+                or ("nrow" in dfn_shape and "ncol" in dfn_shape)
+            ):
+                shape.append("nmesh_face")
+            elif "nrow" in dfn_shape:
+                shape.append("y")
+            elif "ncol" in dfn_shape:
+                shape.append("x")
+            return shape
+
+        # dtype
+        _meta["dtype"] = spec.arrays[param].dtype
+        if np.issubdtype(spec.arrays[param].dtype, np.floating):
+            _meta["encodings"]["_FillValue"] = (
+                FILL_DNODATA if metadata(spec.arrays[param], "block") == "period" else FILL_FLOAT64
+            )
+        elif np.issubdtype(spec.arrays[param].dtype, np.integer):
+            _meta["encodings"]["_FillValue"] = (
+                # FILL_DNODATA  # TODO: FILL_INODATA
+                FILL_INT64
+            )
+
+        # modflow_input internal attribute
+        ptype = context["package_type"].split("-")[1].strip()
+        _meta["attrs"]["modflow_input"] = (
+            f"{modelname}/{context['package_name']}/{param}"
+            if multi_package(context["package_type"])
+            else f"{modelname}/{ptype}/{param}"
+        )
+
+        # data dims
+        _meta["shape"] = (
+            _mesh_shape(spec.arrays[param].dims)
+            if mesh is not None
+            else _structured_shape(spec.arrays[param].dims)
+        )
+
+        # optional data
+        if "data" in _meta:
+            dims = [context["dimmap"][dim] for dim in _meta["shape"]]
+            nval = np.prod(dims)
+
+            if "layer" in _meta["attrs"]:
+                data = _meta["data"]
+                layer = _meta["attrs"]["layer"] - 1
+                if data.size == nval * context["dimmap"]["z"]:
+                    # provided data is for full grid
+                    s = dims
+                    if "nodes" in spec.arrays[param].dims:  # type: ignore
+                        if _meta["shape"][0] == "time":
+                            s.insert(1, context["dimmap"]["z"])
+                            _meta["data"] = data.reshape(s)[:, layer, :]
+                        else:
+                            s.insert(0, context["dimmap"]["z"])
+                            _meta["data"] = data.reshape(s)[layer, :].ravel()
+                    elif "nlay" in spec.arrays[param].dims:  # type: ignore
+                        s.insert(0, context["dimmap"]["z"])
+                        _meta["data"] = data.reshape(s)[layer, :].ravel()
+                else:
+                    # assume provided data is correctly formatted
+                    pass
+
+            else:
+                data = _meta["data"]
+                assert data.size == nval
+                _meta["data"] = data.reshape(dims)
+
+        return _meta
+
+
+class NetCDFParamAttrs(BaseModel):
+    modflow_input: str = Field()
+    modflow_iaux: int | None = Field(default=None)
+    layer: int | None = Field(default=None)
+
+    model_config = ConfigDict(extra="allow")
+
+    @field_validator("modflow_input", mode="before")
+    @classmethod
+    def validate_modflow_input(cls, v: str, info: ValidationInfo) -> str:
+        """
+        validate parameter modflow_input attribute
+        """
+        v = v.lower()
+        modelname = info.context.get("modelname")  # type: ignore
+        if v.split("/")[0] != modelname:
+            raise ValueError(
+                f'modflow_input attribute "{v}" does not match dataset modelname "{modelname}")'
+            )
+        return v
+
+    @field_validator("modflow_iaux", mode="before")
+    @classmethod
+    def validate_modflow_iaux(cls, v: int, info: ValidationInfo) -> int:
+        """
+        validate parameter modflow_iaux attribute
+        """
+        return v
+
+    @field_validator("layer", mode="before")
+    @classmethod
+    def validate_layer(cls, v: int, info: ValidationInfo) -> int:
+        """
+        validate parameter layer attribute
+        """
+        dims = info.context.get("dims")  # type: ignore
+        if v is not None and v > dims[1]:
+            raise ValueError(f"param layer attribute value {v} exceeds grid k")
+        return v
+
+
+class NetCDFParamEncodings(BaseModel):
+    fill: float = Field(alias="_FillValue")
+
+    model_config = ConfigDict(extra="allow")
+
+    @field_validator("fill", mode="before")
+    @classmethod
+    def validate_fill(cls, v: float, info: ValidationInfo) -> float:
+        """
+        validate parameter fill (_FillValue) encoding attribute
+        """
+        return v
diff --git a/pixi.lock b/pixi.lock
index da08067..45a9563 100644
--- a/pixi.lock
+++ b/pixi.lock
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
+      - pypi: https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl
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       - conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h8210216_2.conda
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       - pypi: https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/bc/d9/5ec15501b675f7bc07c5d16aa70d8d778b12375686b6efd47656efdc67cd/url_normalize-2.2.1-py3-none-any.whl
@@ -3974,6 +4054,13 @@ packages:
   purls: []
   size: 23621
   timestamp: 1650670423406
+- pypi: https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl
+  name: annotated-types
+  version: 0.7.0
+  sha256: 1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53
+  requires_dist:
+  - typing-extensions>=4.0.0 ; python_full_version < '3.9'
+  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl
   name: anyio
   version: 4.11.0
@@ -5318,7 +5405,7 @@ packages:
 - pypi: ./
   name: flopy4
   version: 0.0.1.dev0
-  sha256: e2bd4bc603e77ab202708920decb6d1ba9ac82b48b6f8f9229fed27374e1ed3f
+  sha256: eadc31c7327ee94947ccda7d487fefd855676fc33a50fcb090a6e05f10c9d378
   requires_dist:
   - modflow-devtools[dfn] @ git+https://github.com/MODFLOW-ORG/modflow-devtools.git@dfn
   - xattree @ git+https://github.com/wpbonelli/xattree.git
@@ -5339,6 +5426,7 @@ packages:
   - netcdf4>=1.7.3,<2
   - pyshp>=3.0.2.post1,<4
   - llvmlite>=0.45.1,<0.46
+  - pydantic
   - flopy4[build,lint,test] ; extra == 'dev'
   - codespell[toml]>=2.2.2 ; extra == 'lint'
   - ruff ; extra == 'lint'
@@ -11389,6 +11477,102 @@ packages:
   version: '2.23'
   sha256: e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934
   requires_python: '>=3.8'
+- pypi: https://files.pythonhosted.org/packages/5a/87/b70ad306ebb6f9b585f114d0ac2137d792b48be34d732d60e597c2f8465a/pydantic-2.12.5-py3-none-any.whl
+  name: pydantic
+  version: 2.12.5
+  sha256: e561593fccf61e8a20fc46dfc2dfe075b8be7d0188df33f221ad1f0139180f9d
+  requires_dist:
+  - annotated-types>=0.6.0
+  - pydantic-core==2.41.5
+  - typing-extensions>=4.14.1
+  - typing-inspection>=0.4.2
+  - email-validator>=2.0.0 ; extra == 'email'
+  - tzdata ; python_full_version >= '3.9' and sys_platform == 'win32' and extra == 'timezone'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/0d/76/941cc9f73529988688a665a5c0ecff1112b3d95ab48f81db5f7606f522d3/pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: eceb81a8d74f9267ef4081e246ffd6d129da5d87e37a77c9bde550cb04870c1c
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/11/66/f14d1d978ea94d1bc21fc98fcf570f9542fe55bfcc40269d4e1a21c19bf7/pydantic_core-2.41.5-cp311-cp311-win_amd64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: 76ee27c6e9c7f16f47db7a94157112a2f3a00e958bc626e2f4ee8bec5c328fbe
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/12/44/37e403fd9455708b3b942949e1d7febc02167662bf1a7da5b78ee1ea2842/pydantic_core-2.41.5-cp311-cp311-macosx_11_0_arm64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: 7f3bf998340c6d4b0c9a2f02d6a400e51f123b59565d74dc60d252ce888c260b
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/5f/5d/5f6c63eebb5afee93bcaae4ce9a898f3373ca23df3ccaef086d0233a35a7/pydantic_core-2.41.5-cp312-cp312-macosx_10_12_x86_64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: f41a7489d32336dbf2199c8c0a215390a751c5b014c2c1c5366e817202e9cdf7
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/86/45/00173a033c801cacf67c190fef088789394feaf88a98a7035b0e40d53dc9/pydantic_core-2.41.5-cp312-cp312-win_amd64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: 1962293292865bca8e54702b08a4f26da73adc83dd1fcf26fbc875b35d81c815
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/87/06/8806241ff1f70d9939f9af039c6c35f2360cf16e93c2ca76f184e76b1564/pydantic_core-2.41.5-cp313-cp313-macosx_10_12_x86_64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: 941103c9be18ac8daf7b7adca8228f8ed6bb7a1849020f643b3a14d15b1924d9
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/94/02/abfa0e0bda67faa65fef1c84971c7e45928e108fe24333c81f3bfe35d5f5/pydantic_core-2.41.5-cp313-cp313-macosx_11_0_arm64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: 112e305c3314f40c93998e567879e887a3160bb8689ef3d2c04b6cc62c33ac34
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/9e/8b/341991b158ddab181cff136acd2552c9f35bd30380422a639c0671e99a91/pydantic_core-2.41.5-cp313-cp313-win_amd64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: 79ec52ec461e99e13791ec6508c722742ad745571f234ea6255bed38c6480f11
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/aa/32/9c2e8ccb57c01111e0fd091f236c7b371c1bccea0fa85247ac55b1e2b6b6/pydantic_core-2.41.5-cp312-cp312-macosx_11_0_arm64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: 070259a8818988b9a84a449a2a7337c7f430a22acc0859c6b110aa7212a6d9c0
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/c8/be/8fed28dd0a180dca19e72c233cbf58efa36df055e5b9d90d64fd1740b828/pydantic_core-2.41.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: f31d95a179f8d64d90f6831d71fa93290893a33148d890ba15de25642c5d075b
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/cf/4e/35a80cae583a37cf15604b44240e45c05e04e86f9cfd766623149297e971/pydantic_core-2.41.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: 406bf18d345822d6c21366031003612b9c77b3e29ffdb0f612367352aab7d586
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/e8/72/74a989dd9f2084b3d9530b0915fdda64ac48831c30dbf7c72a41a5232db8/pydantic_core-2.41.5-cp311-cp311-macosx_10_12_x86_64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: a3a52f6156e73e7ccb0f8cced536adccb7042be67cb45f9562e12b319c119da6
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/b8/20/e989ab34f456f2f86a8d1e9013d09cc022e7508868e452084f49210b912e/pydap-3.5.8-py3-none-any.whl
   name: pydap
   version: 3.5.8
@@ -13208,6 +13392,13 @@ packages:
   version: 4.15.0
   sha256: f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548
   requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl
+  name: typing-inspection
+  version: 0.4.2
+  sha256: 4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7
+  requires_dist:
+  - typing-extensions>=4.12.0
+  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl
   name: tzdata
   version: '2025.2'
diff --git a/pyproject.toml b/pyproject.toml
index c214ce0..0985e0d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,6 +53,7 @@ dependencies = [
     "netcdf4>=1.7.3,<2",
     "pyshp>=3.0.2.post1,<4",
     "llvmlite>=0.45.1,<0.46",
+    "pydantic",
 ]
 dynamic = ["version"]
 
diff --git a/test/test_mf6_component.py b/test/test_mf6_component.py
index 1b82b24..31c00e4 100644
--- a/test/test_mf6_component.py
+++ b/test/test_mf6_component.py
@@ -450,6 +450,8 @@ def test_quickstart_grid(function_tmpdir):
 
 
 def test_quickstart_netcdf(function_tmpdir):
+    from flopy4.mf6.netcdf import NetCDFModel
+
     sim_name = "quickstart"
     gwf_name = "mymodel"
 
@@ -498,7 +500,8 @@ def test_quickstart_netcdf(function_tmpdir):
     nc_fpth = function_tmpdir / f"{gwf_name}.input.nc"
     gwf.netcdf_file = nc_fpth
 
-    ds = gwf.to_xarray(format="structured")
+    nc_model = NetCDFModel.from_model(gwf)
+    ds = nc_model.to_xarray()
     ds.to_netcdf(nc_fpth)
 
     with WriteContext(use_netcdf=True):
@@ -526,7 +529,7 @@ def test_quickstart_netcdf(function_tmpdir):
         lines = fh.readlines()
         assert " HEAD NETCDF\n" in lines
 
-    ds = xr.load_dataset(nc_fpth)
+    ds = xr.load_dataset(nc_fpth, mask_and_scale=False)
     assert ("dis_delr") in ds
     assert ("dis_delc") in ds
     assert ("dis_top") in ds
@@ -552,6 +555,8 @@ def test_quickstart_netcdf(function_tmpdir):
 
 
 def test_quickstart_netcdf_mesh(function_tmpdir):
+    from flopy4.mf6.netcdf import NetCDFModel
+
     sim_name = "quickstart"
     gwf_name = "mymodel"
 
@@ -600,7 +605,8 @@ def test_quickstart_netcdf_mesh(function_tmpdir):
     nc_fpth = function_tmpdir / f"{gwf_name}.input.nc"
     gwf.netcdf_file = nc_fpth
 
-    ds = gwf.to_xarray(format="layered")
+    nc_model = NetCDFModel.from_model(gwf, mesh="layered")
+    ds = nc_model.to_xarray()
     ds.to_netcdf(nc_fpth)
 
     with WriteContext(use_netcdf=True):
@@ -628,7 +634,7 @@ def test_quickstart_netcdf_mesh(function_tmpdir):
         lines = fh.readlines()
         assert " HEAD NETCDF\n" in lines
 
-    ds = xr.load_dataset(nc_fpth)
+    ds = xr.load_dataset(nc_fpth, mask_and_scale=False)
     assert ("dis_delr") in ds
     assert ("dis_delc") in ds
     assert ("dis_top") in ds
diff --git a/test/test_mf6_netcdf.py b/test/test_mf6_netcdf.py
new file mode 100644
index 0000000..51fdfbb
--- /dev/null
+++ b/test/test_mf6_netcdf.py
@@ -0,0 +1,374 @@
+import numpy as np
+import xarray as xr
+
+from flopy4.mf6.netcdf import (
+    FILL_DNODATA,
+    FILL_FLOAT64,
+    FILL_INT64,
+    NetCDFModel,
+    NetCDFPackage,
+    NetCDFParam,
+)
+
+
+def grid_nodata(dims):
+    grid = np.full((dims[1], dims[2], dims[3]), FILL_DNODATA, dtype=float)
+    return np.repeat(np.expand_dims(grid, axis=0), repeats=dims[0], axis=0)
+
+
+def layer_nodata(dims):
+    layer = np.full((dims[2], dims[3]), FILL_DNODATA, dtype=float)
+    return np.repeat(np.expand_dims(layer, axis=0), repeats=dims[0], axis=0)
+
+
+def test_model_nomesh():
+    dims = [2, 4, 3, 2]  # [nper, nlay, nrow, ncol]
+    welg_0_q = grid_nodata(dims)
+    welg_0_q[0, ...] = np.linspace(0, 1, 24).reshape([4, 3, 2])
+    rcha_0_recharge = layer_nodata(dims)
+    rcha_0_recharge[1, ...] = np.linspace(1, 2, 6).reshape([3, 2])
+
+    packages = [
+        {
+            "package_name": "welg_0",
+            "package_type": "gwf-wElg",
+            "auxiliary": ["concentration"],
+            "params": [
+                {"name": "aux"},
+                {"name": "q", "data": welg_0_q},
+            ],
+        },
+        {
+            "package_name": "rcha_0",
+            "package_type": "gwf-rcha",
+            "params": [
+                {"name": "recharge", "data": rcha_0_recharge},
+            ],
+        },
+    ]
+    nc_cfg = {
+        "modeltype": "gwf6",
+        "modelname": "gwFmodel",
+        "gridtype": "structured",
+        "packages": packages,
+    }
+
+    # classmethod to generate and validate model
+    nc_model = NetCDFModel.from_dict(nc_cfg, context={"dims": dims})
+    meta = nc_model.meta
+    assert isinstance(meta, dict)
+
+    # dataset from model instance
+    ds = nc_model.to_xarray()
+    assert isinstance(ds, xr.Dataset)
+
+    assert ds.attrs["modflow_grid"] == "structured"
+    assert ds.attrs["modflow_model"] == "gwf6: gwfmodel"
+    assert "mesh" not in ds.attrs
+    assert "welg_0_q" in ds
+    assert "welg_0_concentration" in ds
+    assert np.allclose(ds["welg_0_q"].values, welg_0_q)
+    assert np.allclose(ds["welg_0_concentration"].values, FILL_DNODATA)
+    assert np.allclose(ds["rcha_0_recharge"].values, rcha_0_recharge)
+    assert ds["welg_0_q"].dims == ("time", "z", "y", "x")
+    assert ds["welg_0_concentration"].dims == ("time", "z", "y", "x")
+    assert ds["rcha_0_recharge"].dims == ("time", "y", "x")
+    assert ds.sizes["time"] == 2
+    assert ds.sizes["z"] == 4
+    assert ds.sizes["y"] == 3
+    assert ds.sizes["x"] == 2
+    assert len(ds) == 3
+
+    context = {
+        "modelname": "gwfmodel",
+        "gridtype": "structured",
+        "dims": [2, 4, 3, 2],
+    }
+
+    for p in packages:
+        nc_pkg = NetCDFPackage.from_dict(
+            p,
+            context=context,
+        )
+        ds = nc_pkg.to_xarray()
+        assert isinstance(ds, xr.Dataset)
+        meta = nc_pkg.meta
+        assert isinstance(meta, dict)
+        nc_pkg = NetCDFPackage.model_validate(meta, context=context)
+
+
+def test_package_nomesh():
+    packages = [
+        {
+            "package_name": "welg_0",
+            "package_type": "gwf-welg",
+            "auxiliary": ["concentration", "temperature"],
+            "params": [
+                {"name": "aux"},
+                {"name": "q"},
+            ],
+        },
+    ]
+
+    context = {"modelname": "gwfmodel", "gridtype": "structured", "dims": [1, 1, 1, 1]}
+
+    nc_pkg = NetCDFPackage.from_dict(packages[0], context=context)
+    meta = nc_pkg.meta
+    assert isinstance(meta, dict)
+    nc_pkg = NetCDFPackage.model_validate(meta, context=context)
+    ds = nc_pkg.to_xarray()
+    assert isinstance(ds, xr.Dataset)
+
+    # assert ds.attrs["modflow_grid"] == "structured"
+    # assert ds.attrs["modflow_model"] == "gwf6: gwfmodel"
+    assert "mesh" not in ds.attrs
+    assert "welg_0_q" in ds
+    assert "welg_0_concentration" in ds
+    assert "welg_0_temperature" in ds
+    assert np.allclose(ds["welg_0_q"].values.ravel(), FILL_DNODATA)
+    assert np.allclose(ds["welg_0_concentration"].values, FILL_DNODATA)
+    assert np.allclose(ds["welg_0_temperature"].values, FILL_DNODATA)
+    assert ds["welg_0_q"].dims == ("time", "z", "y", "x")
+    assert ds["welg_0_concentration"].dims == ("time", "z", "y", "x")
+    assert ds["welg_0_temperature"].dims == ("time", "z", "y", "x")
+    assert ds.sizes["time"] == 1
+    assert ds.sizes["z"] == 1
+    assert ds.sizes["y"] == 1
+    assert ds.sizes["x"] == 1
+    assert len(ds) == 3
+
+
+def test_param_nomesh():
+    params = [
+        {"name": "aux", "attrs": {"modflow_iaux": 1}},
+        {"name": "q"},
+    ]
+    context = {
+        "modelname": "gwfmodel",
+        "gridtype": "structured",
+        "package_name": "welg0",
+        "package_type": "gwf-welg",
+        "dims": [1, 1, 1, 1],
+    }
+
+    for p in params:
+        nc_param = NetCDFParam.from_dict(p, context=context)
+        ds = nc_param.to_xarray()
+        assert isinstance(ds, xr.Dataset)
+        meta = nc_param.meta
+        assert isinstance(meta, dict)
+        nc_param = NetCDFParam.model_validate(meta, context=context)
+
+
+def test_model_mesh():
+    dims = [2, 4, 3, 2]  # [nper, nlay, nrow, ncol]
+    dis_botm = np.linspace(11, 21, 24)
+    npf_k = np.linspace(0, 10, 24)
+    welg_0_q = grid_nodata(dims)
+    welg_0_q[0, ...] = np.linspace(0, 1, 24).reshape([4, 3, 2])
+    rcha_0_recharge = np.linspace(1, 2, 12)
+    packages = [
+        {
+            "package_name": "dis",
+            "package_type": "gwf-dis",
+            "params": [
+                {"name": "delr"},
+                {"name": "delc"},
+                {"name": "idomain"},
+                {"name": "botm", "data": dis_botm},
+            ],
+        },
+        {
+            "package_name": "npf",
+            "package_type": "gwf-npf",
+            "params": [
+                {"name": "icelltype"},
+                {"name": "k", "data": npf_k},
+                {"name": "k22"},
+            ],
+        },
+        {
+            "package_name": "welg_0",
+            "package_type": "gwf-welg",
+            "auxiliary": ["concentration"],
+            "params": [
+                {"name": "aux"},
+                {"name": "q", "data": welg_0_q},
+            ],
+        },
+        {
+            "package_name": "rcha_0",
+            "package_type": "gwf-rcha",
+            "params": [
+                {"name": "recharge", "data": rcha_0_recharge},
+            ],
+        },
+    ]
+    nc_cfg = {
+        "modeltype": "gwf6",
+        "modelname": "gwfmodel",
+        "gridtype": "structured",
+        "attrs": {"mesh": "layered"},
+        "packages": packages,
+    }
+
+    # classmethod to generate and validate model
+    nc_model = NetCDFModel.from_dict(nc_cfg, context={"dims": dims})
+
+    # dataset from model instance
+    ds = nc_model.to_xarray()
+
+    assert ds.attrs["modflow_grid"] == "structured"
+    assert ds.attrs["modflow_model"] == "gwf6: gwfmodel"
+    assert ds.attrs["mesh"] == "layered"
+    assert "dis_delr" in ds
+    assert "dis_delc" in ds
+    assert "rcha_0_recharge" in ds
+    assert np.allclose(ds["dis_delr"].values, FILL_FLOAT64)
+    assert np.allclose(ds["dis_delc"].values, FILL_FLOAT64)
+    assert np.allclose(ds["rcha_0_recharge"].values.ravel(), rcha_0_recharge)
+    assert ds["dis_delr"].dims == ("x",)
+    assert ds["dis_delc"].dims == ("y",)
+    assert ds["rcha_0_recharge"].dims == ("time", "nmesh_face")
+    for layer in range(4):
+        assert f"dis_idomain_l{layer + 1}" in ds
+        assert f"dis_botm_l{layer + 1}" in ds
+        assert f"npf_k_l{layer + 1}" in ds
+        assert f"npf_k22_l{layer + 1}" in ds
+        assert f"npf_icelltype_l{layer + 1}" in ds
+        assert f"welg_0_q_l{layer + 1}" in ds
+        assert f"welg_0_concentration_l{layer + 1}" in ds
+        assert np.allclose(ds[f"dis_idomain_l{layer + 1}"].values, FILL_INT64)
+        assert np.allclose(ds[f"dis_botm_l{layer + 1}"].values, dis_botm.reshape([4, 6])[layer, :])
+        assert np.allclose(ds[f"npf_k_l{layer + 1}"].values, npf_k.reshape([4, 6])[layer, :])
+        assert np.allclose(ds[f"npf_k22_l{layer + 1}"].values, FILL_FLOAT64)
+        assert np.allclose(ds[f"npf_icelltype_l{layer + 1}"].values, FILL_INT64)
+        assert np.allclose(
+            ds[f"welg_0_q_l{layer + 1}"].values,
+            welg_0_q.reshape([2, 4, 6])[:, layer, :],
+        )
+        assert np.allclose(ds[f"welg_0_concentration_l{layer + 1}"].values, FILL_DNODATA)
+        assert ds[f"dis_idomain_l{layer + 1}"].dims == ("nmesh_face",)
+        assert ds[f"npf_k_l{layer + 1}"].dims == ("nmesh_face",)
+        assert ds[f"npf_k22_l{layer + 1}"].dims == ("nmesh_face",)
+        assert ds[f"npf_icelltype_l{layer + 1}"].dims == ("nmesh_face",)
+        assert ds[f"welg_0_q_l{layer + 1}"].dims == ("time", "nmesh_face")
+        assert ds[f"welg_0_concentration_l{layer + 1}"].dims == ("time", "nmesh_face")
+    assert ds.sizes["time"] == 2
+    assert ds.sizes["nmesh_face"] == 6
+    assert ds.sizes["x"] == 2
+    assert ds.sizes["y"] == 3
+    assert len(ds) == 31
+
+
+def test_package_mesh():
+    packages = [
+        {
+            "package_name": "npf",
+            "package_type": "gwf-npf",
+            "params": [
+                {"name": "icelltype"},
+                {"name": "k"},
+                {"name": "k22"},
+            ],
+        },
+        {
+            "package_name": "welg_0",
+            "package_type": "gwf-welg",
+            "auxiliary": ["concentration"],
+            "params": [
+                {"name": "aux"},
+                {"name": "q"},
+            ],
+        },
+        {
+            "package_name": "rcha0",
+            "package_type": "gwf-rcha",
+            "params": [
+                {"name": "recharge"},
+                {"name": "irch"},
+            ],
+        },
+    ]
+
+    context = {
+        "mesh": "layered",
+        "modelname": "gwfmodel",
+        "gridtype": "structured",
+        "dims": [2, 3, 2, 2],
+    }
+    for p in packages:
+        nc_pkg = NetCDFPackage.from_dict(p, context=context)
+        ds = nc_pkg.to_xarray()
+        assert isinstance(ds, xr.Dataset)
+        meta = nc_pkg.meta
+        assert isinstance(meta, dict)
+        nc_pkg = NetCDFPackage.model_validate(meta, context=context)
+
+        if p["package_type"] == "gwf-npf":
+            # TODO: still write model attrs?
+            assert len(ds.attrs) == 0
+            for layer in range(3):
+                assert f"npf_k_l{layer + 1}" in ds
+                assert f"npf_k22_l{layer + 1}" in ds
+                assert f"npf_icelltype_l{layer + 1}" in ds
+                assert np.allclose(ds[f"npf_k_l{layer + 1}"].values, FILL_FLOAT64)
+                assert np.allclose(ds[f"npf_k22_l{layer + 1}"].values, FILL_FLOAT64)
+                assert np.allclose(ds[f"npf_icelltype_l{layer + 1}"].values, FILL_INT64)
+                assert ds[f"npf_k_l{layer + 1}"].dims == ("nmesh_face",)
+                assert ds[f"npf_k22_l{layer + 1}"].dims == ("nmesh_face",)
+                assert ds[f"npf_icelltype_l{layer + 1}"].dims == ("nmesh_face",)
+            assert ds.sizes["nmesh_face"] == 4
+            assert len(ds) == 9
+
+        elif p["package_type"] == "gwf-welg":
+            # TODO: still write model attrs?
+            assert len(ds.attrs) == 0
+            for layer in range(3):
+                assert f"welg_0_q_l{layer + 1}" in ds
+                assert f"welg_0_concentration_l{layer + 1}" in ds
+                assert np.allclose(ds[f"welg_0_q_l{layer + 1}"].values, FILL_DNODATA)
+                assert np.allclose(ds[f"welg_0_concentration_l{layer + 1}"].values, FILL_DNODATA)
+                assert ds[f"welg_0_q_l{layer + 1}"].dims == ("time", "nmesh_face")
+                assert ds[f"welg_0_concentration_l{layer + 1}"].dims == ("time", "nmesh_face")
+            assert ds.sizes["time"] == 2
+            assert ds.sizes["nmesh_face"] == 4
+            assert len(ds) == 6
+
+        elif p["package_type"] == "gwf-rcha":
+            # TODO: still write model attrs?
+            assert len(ds.attrs) == 0
+            assert "rcha0_recharge" in ds
+            assert "rcha0_irch" in ds
+            assert np.allclose(ds["rcha0_recharge"].values, FILL_DNODATA)
+            assert np.allclose(ds["rcha0_irch"].values, FILL_INT64)
+            assert ds["rcha0_recharge"].dims == ("time", "nmesh_face")
+            assert ds["rcha0_irch"].dims == ("time", "nmesh_face")
+            assert ds.sizes["time"] == 2
+            assert ds.sizes["nmesh_face"] == 4
+            assert len(ds) == 2
+
+
+def test_param_mesh():
+    params = [
+        {"name": "aux", "attrs": {"layer": 1, "modflow_iaux": 1}},
+        {"name": "aux", "attrs": {"layer": 2, "modflow_iaux": 1}},
+        {"name": "q", "attrs": {"layer": 1}},
+        {"name": "q", "attrs": {"layer": 2}},
+    ]
+    context = {
+        "mesh": "layered",
+        "modelname": "gwfmodel",
+        "gridtype": "structured",
+        "package_name": "welg0",
+        "package_type": "gwf-welg",
+        "dims": [1, 2, 1, 1],
+    }
+
+    for p in params:
+        nc_param = NetCDFParam.from_dict(p, context=context)
+        ds = nc_param.to_xarray()
+        assert isinstance(ds, xr.Dataset)
+        meta = nc_param.meta
+        assert isinstance(meta, dict)
+        nc_param = NetCDFParam.model_validate(meta, context=context)