Suppose $F_{(1)}(x)$ is the cumulative distribution function of $\chi^2_{(1)}$, and $F^{-1}_{(1)}(x)$ its inverse i.e. quantile function, and $F_{(2)}(x)$ is the cumulative distribution function of $\chi^2_{(2)}$.
Then
- if $X_1 \sim \chi^2_{(1)}$ so $F_{(1)}(X_1)\sim Unif(0,1)$
- and $X_2 =F^{-1}_{(2)}\left(F_{(1)}(X_1)\right)-X_1$ so $X_2$ is determined by $X_1$ and thus not independent
- then $X_3 =X_1+X_2 \sim \chi^2_{(2)}$
$X_2 \not \sim \chi^2_{(1)}$: for example it has a variance of about $0.409$ while $X_1$ has a variance of $2$ and $X_3$ has a variance of $4$
requested in comments:
This $X_2$ is non-negative because you have $F_{(1)}(x) \ge F_{(2)}(x)$ and then apply a non-decreasing function gives $F^{-1}_{(2)}\left(F_{(1)}(x)\right) \ge x$ and thus $X_2 =F^{-1}_{(2)}\left(F_{(1)}(X_1)\right)-X_1\ge 0$