Begin by reviewing Tempo's documentation to understand its native data export capabilities. Determine if there is a built-in feature for exporting data to a CSV or any other format that can be easily manipulated and converted to JSON.

Use Tempo's export functionality to download the data you need. Typically, this will involve selecting the type of data (e.g., timesheets, reports) and the desired time range. Export the data in the available format, which is often CSV.

Ensure that you have a programming environment set up on your local machine that can process CSV files and output JSON. Python is a popular choice for this task. Make sure you have Python installed along with necessary libraries like `pandas` for data manipulation and `json` for writing JSON files.

Use Python to read the CSV file. You can utilize the `pandas` library to load your CSV into a DataFrame with a command like `df = pandas.read_csv('exported_data.csv')`. This will allow you to easily manipulate and inspect the data.

Check the structure of the DataFrame and make any necessary transformations. This could include renaming columns, filtering rows, or aggregating data to match the desired structure of your JSON file.

Once the data is appropriately structured, convert the DataFrame to JSON. Use the `to_json()` method provided by `pandas` with a command like `json_data = df.to_json(orient='records')`. This will give you a JSON string that can be written to a file.

Write the JSON string to a local file using Python's built-in `open` function. You can do this with a command like:
```python
with open('local_data.json', 'w') as json_file:
json_file.write(json_data)
```
This will save your data in a JSON format on your local machine, completing the transfer from Tempo to a local JSON file.