Start by exporting your data from Redshift to Amazon S3. You can use the `UNLOAD` command to achieve this. This command exports the result of a query to one or more text files in an S3 bucket. Ensure you have the necessary permissions to write to S3.

```sql
UNLOAD ('SELECT FROM your_table')
TO 's3://your-bucket/your-folder/data_'
CREDENTIALS 'aws_access_key_id=YOUR_ACCESS_KEY_ID;aws_secret_access_key=YOUR_SECRET_ACCESS_KEY'
DELIMITER ','
ADDQUOTES
ALLOWOVERWRITE;
```

Once the data is in S3, download it to a local storage system where you can access it. Use the AWS CLI to download the files. Ensure the AWS CLI is configured with the necessary credentials.

```bash
aws s3 cp s3://your-bucket/your-folder/ ./local-folder/ --recursive
```

After downloading, you may need to preprocess the data to make it compatible with ClickHouse. Ensure that the CSV files have headers and are in a format that ClickHouse can understand. You might need to perform data cleaning or transformation at this stage using tools like `sed`, `awk`, or Python scripts.

Set up a corresponding table in ClickHouse that matches the schema of your Redshift table. Make sure that the data types and column names are correctly specified in ClickHouse.

```sql
CREATE TABLE your_clickhouse_table (
column1 DataType1,
column2 DataType2,
...
) ENGINE = MergeTree()
ORDER BY (column1);
```

Use the ClickHouse client to import the CSV files into your ClickHouse table. You can use the `clickhouse-client` command-line tool for this purpose.

```bash
clickhouse-client --query="INSERT INTO your_clickhouse_table FORMAT CSV" < ./local-folder/data_file.csv
```

Repeat this step for each file you need to import.

After importing, verify that the data has been correctly transferred by running queries in ClickHouse to compare with the original data in Redshift. Check for row counts and sample data to ensure accuracy.

```sql
SELECT COUNT() FROM your_clickhouse_table;
```

Once the data is successfully imported, consider optimizing your ClickHouse table for performance. You may want to adjust the table engine settings or modify indices based on query patterns. Additionally, set up monitoring to ensure the data remains consistent and the performance meets your expectations.

```sql
OPTIMIZE TABLE your_clickhouse_table FINAL;
```

This guide covers the manual process of migrating data from Redshift to ClickHouse by leveraging built-in capabilities and command-line tools, ensuring a straightforward and direct approach without third-party tools.