Tree complexity in xgboost
In the paragraph following equation (1):

$T$ is the number of leaves in the tree.

$\gamma$ is a hyperparameter that affects how much regularization occurs on the size (number of leaves) of the tree.
Now it turns out that you can interpret $\gamma$ (at least roughly, see note at bottom) as ([source]):

Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger gamma is, the more conservative the algorithm will be.

You can see that from equation (2), the regularized objective:
$$\mathcal{L}(\phi) = \sum_i l(\hat{y}_i, y_i) + \sum_k \Omega(f_k),\\
\text{where }\Omega(f)=\gamma T + \frac12 \lambda \|w\|^2.$$
By making the split, you increase $T$ by one, so the penalty increases by $\gamma$, and so your base loss term $l$ needs to decrease by at least $\gamma$ for this to be an overall improvement.  Note: Of course, this ignores what happens to the leaf weights $w$ in splitting one node into two.